缺少循环写入文件

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。但是，CSV文件仅为COP提供数据，而不是用于其他股票（csv文件只有一行数据，我假设它是覆盖）。有人可以告诉我我失踪或修复此代码吗？打印（optionsTable）打印4行，但..缺少循环写入文件

这里是输出：

[['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']]

来源

2016-06-10 showri

这段代码很混乱。使用库存列表中的库存列表：将库存列表中的库存列表的含义从列表中更改为列表中的单个项目（它不会中断迭代，因为在'库存列表'被反弹之前它已经开始，但它是仍然是一个非常糟糕的主意）。 – Blckknght

我想下载雅虎财经的股票数据清单 – showri

该脚本基本上下载股票信息，如市值，股息，EPS ..如果我们运行脚本输出将显示列名和数据。 – showri

您可以附加到文件做

with open('test.csv', 'a') as f: 
    my_df.to_csv(f, header=False)

这

来源

2016-06-10 20:16:49 KRONWALLED

你每次循环覆盖您的csv。你应该收集的所有数据和循环后其写入到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)

来源

2016-06-10 20:53:55

非常感谢，这工作完美.. – showri

你可能会考虑使用python的'csv'模块，当你没有做大量的数据操作 –

我会建议你使用pandas-datareader，这是专为你准备做的事情。

这里是一个小的演示：

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 - 是熊猫panel，使我们可以做有趣的事情：

让我们来看看我们有什么在我们的面板

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

来源

2016-06-11 12:52:58 MaxU

缺少循环写入文件

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