import urllib.request
import re
import csv
import pandas as pd
from bs4 import BeautifulSoup
stocklist = ['aapl','goog','fb','amzn','COP']
for stocklist in stocklist:
optionsUrl = urllib.request.urlopen('http://finance.yahoo.com/q?s='+stocklist).read()
soup = BeautifulSoup(optionsUrl)
stocksymbol = ['Symbol:',''+stocklist+'']
optionsTable = [stocksymbol]+[
[x.text for x in y.parent.contents]
for y in soup.findAll('td', attrs={'class': 'yfnc_tabledata1','rtq_table': ''})
]
print(optionsTable)
my_df = pd.DataFrame(optionsTable).T
my_df.to_csv('test.csv', index=False, header=False)
我有这段代码。有人建议我使用熊猫。我能够将列表中的数据写入CSV文件。但CSV文件只有COP的数据，但没有其他股票的数据（csv文件只有一行数据，我假设它被覆盖）。有人可以告诉我我缺少什么或修复此代码？ print（optionsTable）虽然打印了4行..
这是输出：
[['Symbol:', 'aapl'], ['Prev Close:', '99.65'], ['Open:', '98.51'], ['Bid:', '98.95 x 1700'], ['Ask:', '98.96 x 1200'], ['1y Target Est:', '124.90'], ['Beta:', '1.48679'], ['Earnings Date:', 'Jul 19 - Jul 25 (Est.)'], ["Day's Range:", '98.48 - 99.35'], ['52wk Range:', '89.47 - 132.97'], ['Volume:', '28,454,663'], ['Avg Vol (3m):', '38,261,900'], ['Market Cap:', '541.57B'], ['P/E (ttm):', '11.01'], ['EPS (ttm):', '8.98'], ['Div & Yield:', '2.28 (2.30%) '], ['Forward P/E (1 yr):', '10.86'], ['P/S (ttm):', '2.40'], ['Ex-Dividend Date:', '05-May-16'], ['Annual EPS Est\n (Sep-16)\n :', '8.28'], ['Quarterly EPS Est\n (Jun-16)\n :', '1.39'], ['Mean Recommendation*:', '1.8'], ['PEG Ratio (5 yr expected):', '1.30']]
[['Symbol:', 'goog'], ['Prev Close:', '728.58'], ['Open:', '719.47'], ['Bid:', '717.60 x 400'], ['Ask:', '717.96 x 100'], ['1y Target Est:', '924.83'], ['Beta:', '1.032'], ['Next Earnings Date:', 'N/A'], ["Day's Range:", '716.43 - 725.86'], ['52wk Range:', '515.18 - 789.87'], ['Volume:', '1,050,710'], ['Avg Vol (3m):', '1,781,050'], ['Market Cap:', '493.43B'], ['P/E (ttm):', '29.25'], ['EPS (ttm):', '24.58'], ['Div & Yield:', 'N/A (N/A) '], ['Forward P/E (1 yr):', 'N/A'], ['P/S (ttm):', '6.41'], ['Ex-Dividend Date:', 'N/A'], ['Annual EPS Est\n (Dec-16)\n :', 'N/A'], ['Quarterly EPS Est\n (Jun-16)\n :', 'N/A'], ['Mean Recommendation*:', '1.8'], ['PEG Ratio (5 yr expected):', 'N/A']]
[['Symbol:', 'fb'], ['Prev Close:', '118.56'], ['Open:', '117.52'], ['Bid:', '116.39 x 800'], ['Ask:', '116.40 x 500'], ['1y Target Est:', '142.87'], ['Beta:', '0.840485'], ['Earnings Date:', 'Jul 27 - Aug 1 (Est.)'], ["Day's Range:", '116.26 - 118.11'], ['52wk Range:', '72.00 - 121.08'], ['Volume:', '17,257,639'], ['Avg Vol (3m):', '25,746,700'], ['Market Cap:', '333.25B'], ['P/E (ttm):', '71.26'], ['EPS (ttm):', '1.64'], ['Div & Yield:', 'N/A (N/A) '], ['Forward P/E (1 yr):', '25.25'], ['P/S (ttm):', '17.16'], ['Ex-Dividend Date:', 'N/A'], ['Annual EPS Est\n (Dec-16)\n :', 'N/A'], ['Quarterly EPS Est\n (Jun-16)\n :', 'N/A'], ['Mean Recommendation*:', '1.7'], ['PEG Ratio (5 yr expected):', 'N/A']]
[['Symbol:', 'amzn'], ['Prev Close:', '727.65'], ['Open:', '722.35'], ['Bid:', '716.25 x 500'], ['Ask:', '716.50 x 100'], ['1y Target Est:', '800.92'], ['Beta:', '1.6465'], ['Earnings Date:', 'Jul 21 - Jul 25 (Est.)'], ["Day's Range:", '714.21 - 724.98'], ['52wk Range:', '422.64 - 731.50'], ['Volume:', '3,161,899'], ['Avg Vol (3m):', '3,948,360'], ['Market Cap:', '338.47B'], ['P/E (ttm):', '295.70'], ['EPS (ttm):', '2.43'], ['Div & Yield:', 'N/A (N/A) '], ['Forward P/E (1 yr):', '72.29'], ['P/S (ttm):', '3.03'], ['Ex-Dividend Date:', 'N/A'], ['Annual EPS Est\n (Dec-16)\n :', '5.38'], ['Quarterly EPS Est\n (Jun-16)\n :', '1.10'], ['Mean Recommendation*:', '1.8'], ['PEG Ratio (5 yr expected):', '2.43']]
[['Symbol:', 'COP'], ['Prev Close:', '46.57'], ['Open:', '45.90'], ['Bid:', '44.47 x 1300'], ['Ask:', '44.48 x 2300'], ['1y Target Est:', '51.23'], ['Beta:', '1.42252'], ['Earnings Date:', 'Jul 28 - Aug 1 (Est.)'], ["Day's Range:", '44.26 - 46.12'], ['52wk Range:', '31.05 - 64.13'], ['Volume:', '8,217,057'], ['Avg Vol (3m):', '8,947,330'], ['Market Cap:', '55.11B'], ['P/E (ttm):', 'N/A'], ['EPS (ttm):', '-4.98'], ['Div & Yield:', '1.98 (4.16%) '], ['Forward P/E (1 yr):', '143.48'], ['P/S (ttm):', '2.11'], ['Ex-Dividend Date:', '18-May-16'], ['Annual EPS Est\n (Dec-16)\n :', '-2.26'], ['Quarterly EPS Est\n (Jun-16)\n :', '-0.67'], ['Mean Recommendation*:', '2.5'], ['PEG Ratio (5 yr expected):', '0.37']]
答案 0 :(得分：2)
每次循环时你都会覆盖你的csv。您应该收集所有数据并在循环后将它们写入csv：
stocklist = ['aapl','goog','fb','amzn','COP']
columns = []
data = []
for s in stocklist:
optionsUrl = urllib.request.urlopen('http://finance.yahoo.com/q?s='+s).read()
soup = BeautifulSoup(optionsUrl, "html.parser")
stocksymbol = ['Symbol:', s]
optionsTable = [stocksymbol]+[
[x.text for x in y.parent.contents]
for y in soup.findAll('td', attrs={'class': 'yfnc_tabledata1','rtq_table': ''})
]
if not columns:
columns = [o[0] for o in optionsTable]
data.append(o[1] for o in optionsTable)
# create DataFrame from data
df = pd.DataFrame(data, columns=columns)
df.to_csv('test.csv', index=False)
答案 1 :(得分：0)
您可以通过
附加到文件with open('test.csv', 'a') as f:
my_df.to_csv(f, header=False)
此
答案 2 :(得分：0)
我建议你使用专为你要做的事情设计的pandas-datareader。
这是一个小型演示：
from datetime import datetime
import pandas_datareader.data as wb
stocklist = ['AAPL','GOOG','FB','AMZN','COP']
start = datetime(2016,6,8)
end = datetime(2016,6,11)
p = wb.DataReader(stocklist, 'yahoo',start,end)
p - 是一只大熊猫panel，我们可以用它做有趣的事情：
让我们看一下我们在面板中的内容
In [388]: p.axes
Out[388]:
[Index(['Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close'], dtype='object'),
DatetimeIndex(['2016-06-08', '2016-06-09', '2016-06-10'], dtype='datetime64[ns]', name='Date', freq='D'),
Index(['AAPL', 'AMZN', 'COP', 'FB', 'GOOG'], dtype='object')]
In [389]: p.keys()
Out[389]: Index(['Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close'], dtype='object')
选择数据
In [390]: p['Adj Close']
Out[390]:
AAPL AMZN COP FB GOOG
Date
2016-06-08 98.940002 726.640015 47.490002 118.389999 728.280029
2016-06-09 99.650002 727.650024 46.570000 118.559998 728.580017
2016-06-10 98.830002 717.909973 44.509998 116.620003 719.409973
In [391]: p['Volume']
Out[391]:
AAPL AMZN COP FB GOOG
Date
2016-06-08 20812700.0 2200100.0 9596700.0 14368700.0 1582100.0
2016-06-09 26419600.0 2163100.0 5389300.0 13823400.0 985900.0
2016-06-10 31462100.0 3409500.0 8941200.0 18412700.0 1206000.0
In [394]: p[:,:,'AAPL']
Out[394]:
Open High Low Close Volume Adj Close
Date
2016-06-08 99.019997 99.559998 98.680000 98.940002 20812700.0 98.940002
2016-06-09 98.500000 99.989998 98.459999 99.650002 26419600.0 99.650002
2016-06-10 98.529999 99.349998 98.480003 98.830002 31462100.0 98.830002
In [395]: p[:,'2016-06-10']
Out[395]:
Open High Low Close Volume Adj Close
AAPL 98.529999 99.349998 98.480003 98.830002 31462100.0 98.830002
AMZN 722.349976 724.979980 714.210022 717.909973 3409500.0 717.909973
COP 45.900002 46.119999 44.259998 44.509998 8941200.0 44.509998
FB 117.540001 118.110001 116.260002 116.620003 18412700.0 116.620003
GOOG 719.469971 725.890015 716.429993 719.409973 1206000.0 719.409973