pandas 您如何从雅虎财经中提取每周历史数据？
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How do you pull WEEKLY historical data from yahoo finance?
提问by mlo
import datetime
import pandas.io.data
sp = pd.io.data.get_data_yahoo('^IXIC',start = datetime.datetime(1972, 1, 3),
end = datetime.datetime(2010, 1, 3))
I have used the above example, but that just pulls DAILY data into a dataframe when I would like to pull weekly. It doesn't seem like get_data_yahoohas a parameter where you can select perhaps from daily, weekly or monthly like the options made available on yahoo itself. Any other packages or ideas that you know of that might be able to facilitate this?
我已经使用了上面的示例，但是当我想每周拉取时，这只是将 DAILY 数据拉入数据帧。它似乎没有get_data_yahoo一个参数，您可以像雅虎本身提供的选项一样从每日、每周或每月中进行选择。您知道的任何其他软件包或想法可能能够促进这一点？
采纳答案by unutbu
You can downsample using the asfreqmethod:
您可以使用以下asfreq方法进行下采样：
sp = sp.asfreq('W-FRI', method='pad')
The padmethod will propagate the last valid observation forward.
该pad方法将向前传播最后一个有效的观察结果。
Using resample(as @tshauck has shown) is another possibility.
Use asfreqif you want to guarantee that the values in your downsample are values found in the original data set. Use resampleif you wish to aggregate groups of rows from the original data set (for example, by taking a mean). reindexmight introduce NaN values if the original data set does not have a value on the date specified by the reindex -- though (as @behzad.nouri points out) you could use method=padto propagate last observations here as well.
使用resample（如@tshauck 所示）是另一种可能性。使用asfreq如果你要保证你的下采样值是原始数据集找到的值。使用resample，如果你想从原始数据集的行集合组（例如，通过采取平均值）。reindex如果原始数据集在重新索引指定的日期没有值，则可能会引入 NaN 值——尽管（正如@behzad.nouri 指出的那样）您也可以method=pad在这里传播最后的观察结果。
回答by Antony
If you check the latest pandas source codeon github, you will see that interval param is included in the latest master branch. You can manually modify your local copy by overwriting the same data.py under your Site-Packages/pandas/io folder
如果您在 github 上查看最新的Pandas 源代码，您会看到间隔参数包含在最新的 master 分支中。您可以通过覆盖 Site-Packages/pandas/io 文件夹下的相同 data.py 来手动修改本地副本
回答by behzad.nouri
you can always reindex to your desired frequency:
你总是可以重新索引到你想要的频率：
sp.reindex( pd.date_range( start=sp.index.min( ),
end=sp.index.max( ),
freq='W-WED' ) ) # weekly, Wednesdays
edit: you may add , method='ffill'to forward fill NaNvalues.
编辑：您可以添加, method='ffill'转发填充NaN值。
As a suggestion, take Wednesdays because that tend to have least missing values. ( i.e. fewer NYSE holidays falls on Wednesday ). I think Yahoo weekly data gives the stock price each Monday, which is worst weekly frequency based on S&P data from 2000 onwards:
作为建议，请选择星期三，因为它的缺失值往往最少。（即星期三的纽约证券交易所假期较少）。我认为雅虎每周数据给出了每周一的股价，这是基于 2000 年以来标准普尔数据的最差每周频率：
import pandas.io.data as web
sp = web.DataReader("^GSPC", "yahoo", start=dt.date( 2000, 1, 1 ) )
weekday = { 0:'MON', 1:'TUE', 2:'WED', 3:'THU', 4:'FRI' }
sp[ 'weekday' ] = list( map( weekday.get, sp.index.dayofweek ) )
sp.weekday.value_counts( )
output:
输出：
WED 722
TUE 717
THU 707
FRI 705
MON 659
回答by tshauck
One option would be to mask on the day of week you want.
一种选择是在您想要的星期几进行屏蔽。
sp[sp.index.dayofweek == 0]
Another option would be to resample.
另一种选择是重新采样。
sp.resample('W', how='mean')
回答by Into Numbers
That's how I convert daily to weekly price data:
这就是我将每日价格数据转换为每周价格数据的方式：
import datetime
import pandas as pd
import pandas_datareader.data as web
start = datetime.datetime(1972, 1, 3)
end = datetime.datetime(2010, 1, 3)
stock_d = web.DataReader('^IXIC', 'yahoo', start, end)
def week_open(array_like):
return array_like[0]
def week_close(array_like):
return array_like[-1]
stock_w = stock_d.resample('W',
how={'Open': week_open,
'High': 'max',
'Low': 'min',
'Close': week_close,
'Volume': 'sum'},
loffset=pd.offsets.timedelta(days=-6))
stock_w = stock_w[['Open', 'High', 'Low', 'Close', 'Volume']]
more info:
更多信息：
https://pandas-datareader.readthedocs.io/en/latest/remote_data.html#yahoo-financehttps://gist.github.com/prithwi/339f87bf9c3c37bb3188
https://pandas-datareader.readthedocs.io/en/latest/remote_data.html#yahoo-finance https://gist.github.com/prithwi/339f87bf9c3c37bb3188
