stock_data_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    id company_name  now_price  profit     등락률   액면가  amount_money    상장주식수  \\\n",
      "0    1         삼성전자      55600     800 -0.0142   100       3319199  5969783   \n",
      "1    2       SK하이닉스      78700       0  0.0000  5000        572938   728002   \n",
      "2    3        NAVER     328500   10500 -0.0310   100        539605   164263   \n",
      "3    4         LG화학     743000   25000 -0.0326  5000        524501    70592   \n",
      "4    5     삼성바이오로직스     773000    6000 -0.0077  2500        511455    66165   \n",
      "5    6         셀트리온     294000    5500 -0.0184  1000        396882   134994   \n",
      "6    7        삼성전자우      47500     400 -0.0084   100        390871   822887   \n",
      "7    8          현대차     172500    4000 -0.0227  5000        368578   213668   \n",
      "8    9          카카오     402000    8000 -0.0195   500        353816    88014   \n",
      "9   10        삼성SDI     439000   13500 -0.0298  5000        301876    68765   \n",
      "10  11       LG생활건강    1453000   30000 -0.0202  5000        226932    15618   \n",
      "11  12        현대모비스     230000    3500 -0.0150  5000        218626    95055   \n",
      "12  13         삼성물산     107500    2000 -0.0183   100        200904   186887   \n",
      "13  14        SK텔레콤     237500    3500 -0.0145   500        191771    80746   \n",
      "14  15        엔씨소프트     813000   22000 -0.0263   500        178486    21954   \n",
      "15  16          기아차      42150     550 -0.0129  5000        170861   405363   \n",
      "16  17          넷마블     197500    3000  0.0154   100        169468    85807   \n",
      "17  18        POSCO     186000    3000 -0.0159  5000        162168    87187   \n",
      "18  19         KB금융      36950     450 -0.0120  5000        153641   415808   \n",
      "19  20           SK     212000    4500 -0.0208   200        149164    70360   \n",
      "20  21       SK바이오팜     190000    8000  0.0440   500        148795    78313   \n",
      "21  22      SK이노베이션     154000    3000  0.0199  5000        142397    92466   \n",
      "22  23         신한지주      29650     350 -0.0117  5000        141548   477397   \n",
      "23  24           LG      80100    2900 -0.0349  5000        138218   172557   \n",
      "24  25         LG전자      84000    1700 -0.0198  5000        137464   163648   \n",
      "25  26         한국전력      20700     350 -0.0166  5000        132887   641964   \n",
      "26  27         삼성생명      62200    2100 -0.0327   500        124400   200000   \n",
      "27  28      삼성에스디에스     157000    2500 -0.0157   500        121483    77378   \n",
      "28  29         KT&G      82700     900 -0.0108  5000        113541   137292   \n",
      "29  30         삼성전기     135000    5000  0.0385  5000        100836    74694   \n",
      "30  31       아모레퍼시픽     169500    2500 -0.0145   500         99087    58458   \n",
      "31  32         삼성화재     182000    6000 -0.0319   500         86222    47375   \n",
      "32  33       하나금융지주      27950     100 -0.0036  5000         83918   300242   \n",
      "33  34        한화솔루션      48500    2800  0.0613  5000         77534   159864   \n",
      "34  35         신풍제약     146000   10500 -0.0671   500         77358    52985   \n",
      "35  36         고려아연     398000    4000 -0.0100  5000         75103    18870   \n",
      "36  37        한온시스템      12650      50 -0.0039   100         67526   533800   \n",
      "37  38        S-Oil      56900     500 -0.0087  2500         64060   112583   \n",
      "38  39        롯데케미칼     183000     500 -0.0027  5000         62724    34275   \n",
      "39  40           KT      23500     300 -0.0126  5000         61361   261112   \n",
      "40  41       우리금융지주       8460     130 -0.0151  5000         61104   722268   \n",
      "41  42       미래에셋대우       9450     270 -0.0278  5000         60982   645316   \n",
      "42  43       한국조선해양      86100    1500 -0.0171  5000         60936    70773   \n",
      "43  44         기업은행       8200      90 -0.0109  5000         60570   738664   \n",
      "44  45       CJ제일제당     396500    5000 -0.0125  5000         59690    15054   \n",
      "45  46          코웨이      79400    1600 -0.0198   500         58597    73800   \n",
      "46  47       포스코케미칼      89900     200  0.0022   500         54828    60988   \n",
      "47  48          오리온     135000    2000 -0.0146   500         53374    39536   \n",
      "48  49      LG디스플레이      14850       0  0.0000  5000         53136   357816   \n",
      "49  50    KODEX 200      31175     440 -0.0139     0         52608   168750   \n",
      "\n",
      "    외국인비율       거래량       PER     ROE  토론실  \n",
      "0   55.82  21982958     17.40    8.69  NaN  \n",
      "1   47.14   4417569     25.06    4.25  NaN  \n",
      "2   55.46   1168713     76.57   10.56  NaN  \n",
      "3   36.32    762299    128.08    1.84  NaN  \n",
      "4   10.20    123765    148.91    4.77  NaN  \n",
      "5   21.22    603813     99.16   11.19  NaN  \n",
      "6   87.57   2835048     14.86     NaN  NaN  \n",
      "7   31.82   2865128     24.86    4.32  NaN  \n",
      "8   32.06   1160453   -229.71   -5.81  NaN  \n",
      "9   41.89    425979    161.28    2.94  NaN  \n",
      "10  45.22     32106     33.14   20.32  NaN  \n",
      "11  43.68    440423     12.53    7.28  NaN  \n",
      "12  14.43    531470     18.78    4.77  NaN  \n",
      "13  35.68    483197     19.49    3.92  NaN  \n",
      "14  49.67    122912     34.24   14.72  NaN  \n",
      "15  38.87   1505135     16.05    6.50  NaN  \n",
      "16  22.59   2553479     81.71    3.54  NaN  \n",
      "17  52.18    284854     17.68    4.18  NaN  \n",
      "18  65.55   1617330      4.83    8.92  NaN  \n",
      "19  22.25    282909   -408.48    4.31  NaN  \n",
      "20   2.14   1249025   -172.73 -313.66  NaN  \n",
      "21  23.67   1592829     -6.31   -0.20  NaN  \n",
      "22  62.46   2268153      4.45    9.09  NaN  \n",
      "23  34.92    359763     17.10    5.87  NaN  \n",
      "24  34.32    968828     33.71    0.22  NaN  \n",
      "25  17.64   4101904    -14.43   -3.42  NaN  \n",
      "26  12.21    982136     13.83    3.26  NaN  \n",
      "27  12.55    152676     23.31   11.76  NaN  \n",
      "28  42.40    401288     11.06   12.39  NaN  \n",
      "29  32.42   1210153     41.55   10.18  NaN  \n",
      "30  31.08    234217     85.22    5.36  NaN  \n",
      "31  47.12    112043     13.91    4.85  NaN  \n",
      "32  64.35   1204355      3.31    8.77  NaN  \n",
      "33  19.99  23353631    -46.81   -4.02  NaN  \n",
      "34   6.83   3116701  4,424.24    0.80  NaN  \n",
      "35  24.24     48656     13.65    9.76  NaN  \n",
      "36  20.88   1654135     40.54   14.98  NaN  \n",
      "37  74.21    254454     -7.82    1.01  NaN  \n",
      "38  29.43    130430     34.92    5.51  NaN  \n",
      "39  45.28    965370     10.24    4.61  NaN  \n",
      "40  26.31   1634070      4.46     NaN  NaN  \n",
      "41  13.28   3654572     11.25    7.56  NaN  \n",
      "42  16.51    318866     57.90    1.40  NaN  \n",
      "43  11.77   1220093      3.93    7.36  NaN  \n",
      "44  22.45     69551      9.98    3.16  NaN  \n",
      "45  60.38    193016     16.69   30.74  NaN  \n",
      "46   7.50    539078    106.01   11.57  NaN  \n",
      "47  44.34     82960     20.14   14.24  NaN  \n",
      "48  21.41   3682602     -1.83  -22.35  NaN  \n",
      "49  12.79   4788126       NaN     NaN  NaN  \n"
     ]
    }
   ],
   "source": [
    "filename = 'data.csv'\n",
    "df = pd.read_csv(filename)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# csv file ㅡ> dict로 바꾸는 법"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnicodeDecodeError",
     "evalue": "'cp949' codec can't decode byte 0xeb in position 36: illegal multibyte sequence",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-8211db955937>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mreader\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Conda\\envs\\lab\\lib\\csv.py\u001b[0m in \u001b[0;36m__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    108\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mline_num\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    109\u001b[0m             \u001b[1;31m# Used only for its side effect.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 110\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfieldnames\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    111\u001b[0m         \u001b[0mrow\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    112\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mline_num\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mline_num\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Conda\\envs\\lab\\lib\\csv.py\u001b[0m in \u001b[0;36mfieldnames\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     95\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fieldnames\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     96\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 97\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fieldnames\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     98\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     99\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mUnicodeDecodeError\u001b[0m: 'cp949' codec can't decode byte 0xeb in position 36: illegal multibyte sequence"
     ]
    }
   ],
   "source": [
    "reader = csv.DictReader(open('data.csv'))\n",
    "\n",
    "result = {}\n",
    "for row in reader:\n",
    "    key = row.pop('Date')\n",
    "    if key in result:\n",
    "        pass\n",
    "    result[key] = row\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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