缺少写入fi的循环

Question

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。有人建议我用熊猫。我能够将列表中的数据写入CSV文件。但是CSV文件只包含COP的数据，而没有其他股票的数据（CSV文件只有一行数据，我假设它正在覆盖）。有人能告诉我我遗漏了什么或者修正这个代码吗？print（optionsTable）打印4行。。在

输出如下：

^{pr2}$

Answer 1

每次循环时都会覆盖csv。应收集所有数据，并在循环后将其写入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)

Answer 2

您可以通过

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

这个

Answer 3

我建议您使用pandas-datareader，它是为您要做的事情而设计的。在

下面是一个小演示：

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-是一只熊猫panel，我们可以用它来做有趣的事情：

让我们看看小组里有什么

^{pr2}$

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