Part 0: Import package¶
In [1]:
import requests as r
import json
import pandas as pd
from datetime import datetime, date
import matplotlib.pyplot as plt
%matplotlib inline
Part 1: 下載一個月台股個股成交資料¶
In [2]:
url = "https://www.twse.com.tw/exchangeReport/STOCK_DAY?response=json&date=20200801&stockNo=2330"
In [3]:
r.get(url)
Out[3]:
<Response [200]>
In [4]:
res = r.get(url)
In [5]:
res.json()
Out[5]:
{'data': [['109/08/03',
'77,288,571',
'32,339,719,000',
'420.50',
'424.00',
'415.00',
'416.00',
'-9.50',
'43,360'],
['109/08/04',
'41,697,445',
'17,600,499,460',
'421.50',
'425.50',
'418.50',
'425.50',
'+9.50',
'22,250'],
['109/08/05',
'47,460,834',
'20,356,413,204',
'430.50',
'433.50',
'426.00',
'429.00',
'+3.50',
'24,715'],
['109/08/06',
'43,443,627',
'18,877,761,758',
'437.50',
'438.00',
'431.00',
'435.00',
'+6.00',
'22,414'],
['109/08/07',
'37,611,388',
'16,218,241,255',
'433.50',
'434.00',
'428.00',
'433.00',
'-2.00',
'19,739'],
['109/08/10',
'36,067,500',
'15,674,843,876',
'427.50',
'437.50',
'427.50',
'435.50',
'+2.50',
'21,342'],
['109/08/11',
'34,545,313',
'14,918,017,903',
'432.00',
'435.00',
'429.00',
'429.00',
'-6.50',
'20,297'],
['109/08/12',
'53,980,474',
'22,721,731,886',
'422.00',
'423.50',
'419.00',
'419.00',
'-10.00',
'32,761'],
['109/08/13',
'45,669,335',
'19,514,322,038',
'428.00',
'429.00',
'424.50',
'429.00',
'+10.00',
'21,499'],
['109/08/14',
'24,259,947',
'10,332,447,763',
'424.00',
'428.00',
'423.00',
'427.00',
'-2.00',
'12,642'],
['109/08/17',
'34,607,731',
'14,930,831,216',
'427.00',
'435.00',
'425.50',
'435.00',
'+8.00',
'20,823'],
['109/08/18',
'45,504,160',
'19,824,413,320',
'440.00',
'440.50',
'432.00',
'433.00',
'-2.00',
'21,119'],
['109/08/19',
'31,486,800',
'13,574,970,200',
'436.00',
'436.50',
'427.50',
'427.50',
'-5.50',
'18,279'],
['109/08/20',
'75,155,235',
'31,353,886,930',
'427.00',
'427.50',
'401.00',
'415.00',
'-12.50',
'45,670'],
['109/08/21',
'41,956,467',
'17,770,610,338',
'421.50',
'427.00',
'420.00',
'424.50',
'+9.50',
'23,059'],
['109/08/24',
'49,199,406',
'21,139,093,710',
'427.50',
'434.50',
'425.00',
'428.00',
'+3.50',
'24,672'],
['109/08/25',
'37,024,899',
'16,076,146,369',
'433.50',
'436.00',
'430.50',
'434.50',
'+6.50',
'22,035'],
['109/08/26',
'41,406,340',
'18,169,230,108',
'435.50',
'442.00',
'435.00',
'442.00',
'+7.50',
'25,305'],
['109/08/27',
'57,206,712',
'25,658,355,402',
'448.00',
'453.50',
'444.00',
'444.00',
'+2.00',
'31,575'],
['109/08/28',
'57,039,210',
'24,868,453,700',
'440.00',
'440.50',
'431.00',
'435.00',
'-9.00',
'35,253'],
['109/08/31',
'88,569,538',
'38,106,113,563',
'437.00',
'439.50',
'426.50',
'426.50',
'-8.50',
'28,294']],
'date': '20200801',
'fields': ['日期', '成交股數', '成交金額', '開盤價', '最高價', '最低價', '收盤價', '漲跌價差', '成交筆數'],
'notes': ['符號說明:+/-/X表示漲/跌/不比價',
'當日統計資訊含一般、零股、盤後定價、鉅額交易，不含拍賣、標購。',
'ETF證券代號第六碼為K、M、S、C者，表示該ETF以外幣交易。'],
'stat': 'OK',
'title': '109年08月 2330 台積電 各日成交資訊'}
In [6]:
stock_json = res.json()
In [7]:
stock_json['data']
Out[7]:
[['109/08/03', '77,288,571', '32,339,719,000', '420.50', '424.00', '415.00', '416.00', '-9.50', '43,360'], ['109/08/04', '41,697,445', '17,600,499,460', '421.50', '425.50', '418.50', '425.50', '+9.50', '22,250'], ['109/08/05', '47,460,834', '20,356,413,204', '430.50', '433.50', '426.00', '429.00', '+3.50', '24,715'], ['109/08/06', '43,443,627', '18,877,761,758', '437.50', '438.00', '431.00', '435.00', '+6.00', '22,414'], ['109/08/07', '37,611,388', '16,218,241,255', '433.50', '434.00', '428.00', '433.00', '-2.00', '19,739'], ['109/08/10', '36,067,500', '15,674,843,876', '427.50', '437.50', '427.50', '435.50', '+2.50', '21,342'], ['109/08/11', '34,545,313', '14,918,017,903', '432.00', '435.00', '429.00', '429.00', '-6.50', '20,297'], ['109/08/12', '53,980,474', '22,721,731,886', '422.00', '423.50', '419.00', '419.00', '-10.00', '32,761'], ['109/08/13', '45,669,335', '19,514,322,038', '428.00', '429.00', '424.50', '429.00', '+10.00', '21,499'], ['109/08/14', '24,259,947', '10,332,447,763', '424.00', '428.00', '423.00', '427.00', '-2.00', '12,642'], ['109/08/17', '34,607,731', '14,930,831,216', '427.00', '435.00', '425.50', '435.00', '+8.00', '20,823'], ['109/08/18', '45,504,160', '19,824,413,320', '440.00', '440.50', '432.00', '433.00', '-2.00', '21,119'], ['109/08/19', '31,486,800', '13,574,970,200', '436.00', '436.50', '427.50', '427.50', '-5.50', '18,279'], ['109/08/20', '75,155,235', '31,353,886,930', '427.00', '427.50', '401.00', '415.00', '-12.50', '45,670'], ['109/08/21', '41,956,467', '17,770,610,338', '421.50', '427.00', '420.00', '424.50', '+9.50', '23,059'], ['109/08/24', '49,199,406', '21,139,093,710', '427.50', '434.50', '425.00', '428.00', '+3.50', '24,672'], ['109/08/25', '37,024,899', '16,076,146,369', '433.50', '436.00', '430.50', '434.50', '+6.50', '22,035'], ['109/08/26', '41,406,340', '18,169,230,108', '435.50', '442.00', '435.00', '442.00', '+7.50', '25,305'], ['109/08/27', '57,206,712', '25,658,355,402', '448.00', '453.50', '444.00', '444.00', '+2.00', '31,575'], ['109/08/28', '57,039,210', '24,868,453,700', '440.00', '440.50', '431.00', '435.00', '-9.00', '35,253'], ['109/08/31', '88,569,538', '38,106,113,563', '437.00', '439.50', '426.50', '426.50', '-8.50', '28,294']]
In [8]:
pd.DataFrame.from_dict(stock_json['data'])
Out[8]:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 109/08/03 | 77,288,571 | 32,339,719,000 | 420.50 | 424.00 | 415.00 | 416.00 | -9.50 | 43,360 |
| 1 | 109/08/04 | 41,697,445 | 17,600,499,460 | 421.50 | 425.50 | 418.50 | 425.50 | +9.50 | 22,250 |
| 2 | 109/08/05 | 47,460,834 | 20,356,413,204 | 430.50 | 433.50 | 426.00 | 429.00 | +3.50 | 24,715 |
| 3 | 109/08/06 | 43,443,627 | 18,877,761,758 | 437.50 | 438.00 | 431.00 | 435.00 | +6.00 | 22,414 |
| 4 | 109/08/07 | 37,611,388 | 16,218,241,255 | 433.50 | 434.00 | 428.00 | 433.00 | -2.00 | 19,739 |
| 5 | 109/08/10 | 36,067,500 | 15,674,843,876 | 427.50 | 437.50 | 427.50 | 435.50 | +2.50 | 21,342 |
| 6 | 109/08/11 | 34,545,313 | 14,918,017,903 | 432.00 | 435.00 | 429.00 | 429.00 | -6.50 | 20,297 |
| 7 | 109/08/12 | 53,980,474 | 22,721,731,886 | 422.00 | 423.50 | 419.00 | 419.00 | -10.00 | 32,761 |
| 8 | 109/08/13 | 45,669,335 | 19,514,322,038 | 428.00 | 429.00 | 424.50 | 429.00 | +10.00 | 21,499 |
| 9 | 109/08/14 | 24,259,947 | 10,332,447,763 | 424.00 | 428.00 | 423.00 | 427.00 | -2.00 | 12,642 |
| 10 | 109/08/17 | 34,607,731 | 14,930,831,216 | 427.00 | 435.00 | 425.50 | 435.00 | +8.00 | 20,823 |
| 11 | 109/08/18 | 45,504,160 | 19,824,413,320 | 440.00 | 440.50 | 432.00 | 433.00 | -2.00 | 21,119 |
| 12 | 109/08/19 | 31,486,800 | 13,574,970,200 | 436.00 | 436.50 | 427.50 | 427.50 | -5.50 | 18,279 |
| 13 | 109/08/20 | 75,155,235 | 31,353,886,930 | 427.00 | 427.50 | 401.00 | 415.00 | -12.50 | 45,670 |
| 14 | 109/08/21 | 41,956,467 | 17,770,610,338 | 421.50 | 427.00 | 420.00 | 424.50 | +9.50 | 23,059 |
| 15 | 109/08/24 | 49,199,406 | 21,139,093,710 | 427.50 | 434.50 | 425.00 | 428.00 | +3.50 | 24,672 |
| 16 | 109/08/25 | 37,024,899 | 16,076,146,369 | 433.50 | 436.00 | 430.50 | 434.50 | +6.50 | 22,035 |
| 17 | 109/08/26 | 41,406,340 | 18,169,230,108 | 435.50 | 442.00 | 435.00 | 442.00 | +7.50 | 25,305 |
| 18 | 109/08/27 | 57,206,712 | 25,658,355,402 | 448.00 | 453.50 | 444.00 | 444.00 | +2.00 | 31,575 |
| 19 | 109/08/28 | 57,039,210 | 24,868,453,700 | 440.00 | 440.50 | 431.00 | 435.00 | -9.00 | 35,253 |
| 20 | 109/08/31 | 88,569,538 | 38,106,113,563 | 437.00 | 439.50 | 426.50 | 426.50 | -8.50 | 28,294 |
In [9]:
stock_df = pd.DataFrame.from_dict(stock_json['data'])
stock_df.head()
Out[9]:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 109/08/03 | 77,288,571 | 32,339,719,000 | 420.50 | 424.00 | 415.00 | 416.00 | -9.50 | 43,360 |
| 1 | 109/08/04 | 41,697,445 | 17,600,499,460 | 421.50 | 425.50 | 418.50 | 425.50 | +9.50 | 22,250 |
| 2 | 109/08/05 | 47,460,834 | 20,356,413,204 | 430.50 | 433.50 | 426.00 | 429.00 | +3.50 | 24,715 |
| 3 | 109/08/06 | 43,443,627 | 18,877,761,758 | 437.50 | 438.00 | 431.00 | 435.00 | +6.00 | 22,414 |
| 4 | 109/08/07 | 37,611,388 | 16,218,241,255 | 433.50 | 434.00 | 428.00 | 433.00 | -2.00 | 19,739 |
In [10]:
stock_json['fields']
Out[10]:
['日期', '成交股數', '成交金額', '開盤價', '最高價', '最低價', '收盤價', '漲跌價差', '成交筆數']
In [11]:
stock_df.columns = stock_json['fields']
In [12]:
stock_df.head()
Out[12]:
| 日期 | 成交股數 | 成交金額 | 開盤價 | 最高價 | 最低價 | 收盤價 | 漲跌價差 | 成交筆數 | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 109/08/03 | 77,288,571 | 32,339,719,000 | 420.50 | 424.00 | 415.00 | 416.00 | -9.50 | 43,360 |
| 1 | 109/08/04 | 41,697,445 | 17,600,499,460 | 421.50 | 425.50 | 418.50 | 425.50 | +9.50 | 22,250 |
| 2 | 109/08/05 | 47,460,834 | 20,356,413,204 | 430.50 | 433.50 | 426.00 | 429.00 | +3.50 | 24,715 |
| 3 | 109/08/06 | 43,443,627 | 18,877,761,758 | 437.50 | 438.00 | 431.00 | 435.00 | +6.00 | 22,414 |
| 4 | 109/08/07 | 37,611,388 | 16,218,241,255 | 433.50 | 434.00 | 428.00 | 433.00 | -2.00 | 19,739 |
Part 2: 下載歷年台股個股成交資料¶
In [13]:
url = "https://www.twse.com.tw/exchangeReport/STOCK_DAY?response=json&date=20200801&stockNo=2330"
In [14]:
month_list = pd.date_range('2020-06-01','2020-08-01', freq='MS').strftime("%Y%m%d").tolist()
In [15]:
for month in month_list:
print(month)
20200601 20200701 20200801
In [16]:
df = pd.DataFrame()
df
Out[16]:
In [17]:
for month in month_list:
url = "https://www.twse.com.tw/exchangeReport/STOCK_DAY?response=json&date="+ month + "&stockNo=2330"
res = r.get(url)
stock_json = res.json()
stock_df = pd.DataFrame.from_dict(stock_json['data'])
df = df.append(stock_df, ignore_index = True)
In [18]:
df.columns = ['日期', '成交股數', '成交金額', '開盤價', '最高價', '最低價', '收盤價', '漲跌價差', '成交筆數']
In [19]:
df.head()
Out[19]:
| 日期 | 成交股數 | 成交金額 | 開盤價 | 最高價 | 最低價 | 收盤價 | 漲跌價差 | 成交筆數 | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 109/06/01 | 37,936,214 | 11,205,227,094 | 294.00 | 296.50 | 293.50 | 295.50 | +3.50 | 15,502 |
| 1 | 109/06/02 | 26,663,587 | 7,908,476,089 | 296.00 | 297.50 | 296.00 | 296.50 | +1.00 | 9,743 |
| 2 | 109/06/03 | 67,894,337 | 20,354,971,677 | 300.00 | 301.00 | 298.00 | 301.00 | +4.50 | 33,062 |
| 3 | 109/06/04 | 47,225,322 | 14,414,983,532 | 305.00 | 306.00 | 304.00 | 306.00 | +5.00 | 24,482 |
| 4 | 109/06/05 | 44,077,262 | 13,655,618,646 | 308.50 | 312.00 | 308.00 | 311.50 | +5.50 | 24,096 |
In [20]:
def get_stock_data(start_year, start_month, end_year, end_month, stock_code):
start_date = str(date(start_year, start_month, 1))
end_date = str(date(end_year, end_month, 1))
month_list = pd.date_range(start_date, end_date, freq='MS').strftime("%Y%m%d").tolist()
df = pd.DataFrame()
for month in month_list:
url = "https://www.twse.com.tw/exchangeReport/STOCK_DAY?response=json&date="+ month + "&stockNo=" + str(stock_code)
res = r.get(url)
stock_json = res.json()
stock_df = pd.DataFrame.from_dict(stock_json['data'])
df = df.append(stock_df, ignore_index = True)
df.columns = ['日期', '成交股數', '成交金額', '開盤價', '最高價', '最低價', '收盤價', '漲跌價差', '成交筆數']
return df
In [21]:
get_stock_data(start_year = 2019, start_month = 6, end_year = 2019, end_month = 8, stock_code = 2330)
Out[21]:
| 日期 | 成交股數 | 成交金額 | 開盤價 | 最高價 | 最低價 | 收盤價 | 漲跌價差 | 成交筆數 | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 108/06/03 | 36,687,092 | 8,651,389,851 | 235.50 | 238.50 | 232.00 | 238.00 | +2.50 | 8,857 |
| 1 | 108/06/04 | 24,443,428 | 5,745,045,809 | 237.50 | 238.00 | 233.00 | 233.00 | -5.00 | 11,779 |
| 2 | 108/06/05 | 35,901,584 | 8,461,930,934 | 238.00 | 238.00 | 234.00 | 235.00 | +2.00 | 12,064 |
| 3 | 108/06/06 | 34,651,731 | 8,008,768,323 | 231.50 | 232.00 | 229.50 | 232.00 | -3.00 | 14,723 |
| 4 | 108/06/10 | 35,521,888 | 8,442,986,620 | 237.50 | 240.00 | 234.50 | 240.00 | +8.00 | 15,588 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 58 | 108/08/26 | 31,740,620 | 7,910,873,500 | 249.00 | 250.50 | 248.50 | 248.50 | -5.50 | 13,388 |
| 59 | 108/08/27 | 53,664,951 | 13,410,582,151 | 250.50 | 251.00 | 248.50 | 250.00 | +1.50 | 8,939 |
| 60 | 108/08/28 | 15,280,882 | 3,852,096,070 | 250.50 | 253.00 | 250.50 | 252.00 | +2.00 | 6,829 |
| 61 | 108/08/29 | 20,085,767 | 5,078,703,588 | 253.00 | 254.00 | 251.00 | 254.00 | +2.00 | 6,596 |
| 62 | 108/08/30 | 35,299,201 | 9,092,482,858 | 256.50 | 259.00 | 256.00 | 259.00 | +5.00 | 13,703 |
63 rows × 9 columns
Part 3: 將下載的個股資料從字串(string)轉型成日期(datetime)及浮點數(float)¶
In [22]:
stock = get_stock_data(start_year = 2019, start_month = 6, end_year = 2019, end_month = 8, stock_code = 2330)
stock.head()
Out[22]:
| 日期 | 成交股數 | 成交金額 | 開盤價 | 最高價 | 最低價 | 收盤價 | 漲跌價差 | 成交筆數 | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 108/06/03 | 36,687,092 | 8,651,389,851 | 235.50 | 238.50 | 232.00 | 238.00 | +2.50 | 8,857 |
| 1 | 108/06/04 | 24,443,428 | 5,745,045,809 | 237.50 | 238.00 | 233.00 | 233.00 | -5.00 | 11,779 |
| 2 | 108/06/05 | 35,901,584 | 8,461,930,934 | 238.00 | 238.00 | 234.00 | 235.00 | +2.00 | 12,064 |
| 3 | 108/06/06 | 34,651,731 | 8,008,768,323 | 231.50 | 232.00 | 229.50 | 232.00 | -3.00 | 14,723 |
| 4 | 108/06/10 | 35,521,888 | 8,442,986,620 | 237.50 | 240.00 | 234.50 | 240.00 | +8.00 | 15,588 |
把日期從字串(string)換成時間(datetime)，並將民國年換成西元年¶
In [23]:
for row in range(stock.shape[0]):
date2 = stock.iloc[row,0].split('/')
stock.iloc[row, 0] = datetime(int(date2[0]) + 1911, int(date2[1]), int(date2[2]))
stock.head(10)
Out[23]:
| 日期 | 成交股數 | 成交金額 | 開盤價 | 最高價 | 最低價 | 收盤價 | 漲跌價差 | 成交筆數 | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 2019-06-03 00:00:00 | 36,687,092 | 8,651,389,851 | 235.50 | 238.50 | 232.00 | 238.00 | +2.50 | 8,857 |
| 1 | 2019-06-04 00:00:00 | 24,443,428 | 5,745,045,809 | 237.50 | 238.00 | 233.00 | 233.00 | -5.00 | 11,779 |
| 2 | 2019-06-05 00:00:00 | 35,901,584 | 8,461,930,934 | 238.00 | 238.00 | 234.00 | 235.00 | +2.00 | 12,064 |
| 3 | 2019-06-06 00:00:00 | 34,651,731 | 8,008,768,323 | 231.50 | 232.00 | 229.50 | 232.00 | -3.00 | 14,723 |
| 4 | 2019-06-10 00:00:00 | 35,521,888 | 8,442,986,620 | 237.50 | 240.00 | 234.50 | 240.00 | +8.00 | 15,588 |
| 5 | 2019-06-11 00:00:00 | 34,691,670 | 8,448,805,962 | 240.00 | 246.00 | 239.00 | 244.50 | +4.50 | 13,157 |
| 6 | 2019-06-12 00:00:00 | 30,409,556 | 7,469,805,086 | 244.50 | 247.50 | 243.00 | 246.00 | +1.50 | 11,457 |
| 7 | 2019-06-13 00:00:00 | 33,731,724 | 8,136,137,708 | 242.50 | 244.00 | 240.00 | 240.00 | -6.00 | 13,552 |
| 8 | 2019-06-14 00:00:00 | 35,403,556 | 8,396,819,832 | 238.50 | 239.50 | 236.00 | 236.00 | -4.00 | 14,911 |
| 9 | 2019-06-17 00:00:00 | 51,692,012 | 12,043,369,475 | 231.50 | 235.00 | 230.50 | 233.00 | -3.00 | 20,943 |
把"成交股數" "成交金額" "開盤價", "最高價", "最低價", "收盤價" "漲跌價差" "成交筆數"帶有逗號的字串(string)換成浮點數(float)¶
In [26]:
for col in [1, 2, 3, 4, 5, 6, 8]:
for row in range(stock.shape[0]):
stock.iloc[row, col] = float(stock.iloc[row,col].replace(',', ''))
stock.head(10)
Out[26]:
| 日期 | 成交股數 | 成交金額 | 開盤價 | 最高價 | 最低價 | 收盤價 | 漲跌價差 | 成交筆數 | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 2019-06-03 00:00:00 | 3.66871e+07 | 8.65139e+09 | 235.5 | 238.5 | 232 | 238 | +2.50 | 8857 |
| 1 | 2019-06-04 00:00:00 | 2.44434e+07 | 5.74505e+09 | 237.5 | 238 | 233 | 233 | -5.00 | 11779 |
| 2 | 2019-06-05 00:00:00 | 3.59016e+07 | 8.46193e+09 | 238 | 238 | 234 | 235 | +2.00 | 12064 |
| 3 | 2019-06-06 00:00:00 | 3.46517e+07 | 8.00877e+09 | 231.5 | 232 | 229.5 | 232 | -3.00 | 14723 |
| 4 | 2019-06-10 00:00:00 | 3.55219e+07 | 8.44299e+09 | 237.5 | 240 | 234.5 | 240 | +8.00 | 15588 |
| 5 | 2019-06-11 00:00:00 | 3.46917e+07 | 8.44881e+09 | 240 | 246 | 239 | 244.5 | +4.50 | 13157 |
| 6 | 2019-06-12 00:00:00 | 3.04096e+07 | 7.46981e+09 | 244.5 | 247.5 | 243 | 246 | +1.50 | 11457 |
| 7 | 2019-06-13 00:00:00 | 3.37317e+07 | 8.13614e+09 | 242.5 | 244 | 240 | 240 | -6.00 | 13552 |
| 8 | 2019-06-14 00:00:00 | 3.54036e+07 | 8.39682e+09 | 238.5 | 239.5 | 236 | 236 | -4.00 | 14911 |
| 9 | 2019-06-17 00:00:00 | 5.1692e+07 | 1.20434e+10 | 231.5 | 235 | 230.5 | 233 | -3.00 | 20943 |
Part 4: 將下載的個股資料另存成csv檔¶
In [27]:
stock.to_csv("2330.csv")
Part 5: 畫出下載個股的走勢圖¶
In [28]:
stock2 = stock[:10]
In [29]:
stock2
Out[29]:
| 日期 | 成交股數 | 成交金額 | 開盤價 | 最高價 | 最低價 | 收盤價 | 漲跌價差 | 成交筆數 | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 2019-06-03 00:00:00 | 3.66871e+07 | 8.65139e+09 | 235.5 | 238.5 | 232 | 238 | +2.50 | 8857 |
| 1 | 2019-06-04 00:00:00 | 2.44434e+07 | 5.74505e+09 | 237.5 | 238 | 233 | 233 | -5.00 | 11779 |
| 2 | 2019-06-05 00:00:00 | 3.59016e+07 | 8.46193e+09 | 238 | 238 | 234 | 235 | +2.00 | 12064 |
| 3 | 2019-06-06 00:00:00 | 3.46517e+07 | 8.00877e+09 | 231.5 | 232 | 229.5 | 232 | -3.00 | 14723 |
| 4 | 2019-06-10 00:00:00 | 3.55219e+07 | 8.44299e+09 | 237.5 | 240 | 234.5 | 240 | +8.00 | 15588 |
| 5 | 2019-06-11 00:00:00 | 3.46917e+07 | 8.44881e+09 | 240 | 246 | 239 | 244.5 | +4.50 | 13157 |
| 6 | 2019-06-12 00:00:00 | 3.04096e+07 | 7.46981e+09 | 244.5 | 247.5 | 243 | 246 | +1.50 | 11457 |
| 7 | 2019-06-13 00:00:00 | 3.37317e+07 | 8.13614e+09 | 242.5 | 244 | 240 | 240 | -6.00 | 13552 |
| 8 | 2019-06-14 00:00:00 | 3.54036e+07 | 8.39682e+09 | 238.5 | 239.5 | 236 | 236 | -4.00 | 14911 |
| 9 | 2019-06-17 00:00:00 | 5.1692e+07 | 1.20434e+10 | 231.5 | 235 | 230.5 | 233 | -3.00 | 20943 |
In [30]:
fig = plt.figure(figsize = (10, 5))
plt.title('2330 Stock Price')
plt.plot(stock2['日期'], stock2['收盤價'])
plt.plot(stock2['日期'], stock2['開盤價'])
plt.legend(['Close', 'Open'])
/Users/hou/anaconda3/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:2062: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead. x[:, None] /Users/hou/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_base.py:248: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead. x = x[:, np.newaxis] /Users/hou/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_base.py:250: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead. y = y[:, np.newaxis]
Out[30]:
<matplotlib.legend.Legend at 0x7feaf94c86a0>
In [ ]: