pandas 从熊猫数据框中删除非工作日行
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Remove non-business days rows from pandas dataframe
提问by vandelay
I have a dataframe with second timeseries data of wheat in df.
我有一个包含小麦的第二个时间序列数据的数据框df。
df = wt["WHEAT_USD"]
2016-05-02 02:00:00+02:00 4.780
2016-05-02 02:01:00+02:00 4.777
2016-05-02 02:02:00+02:00 4.780
2016-05-02 02:03:00+02:00 4.780
2016-05-02 02:04:00+02:00 4.780
Name: closeAsk, dtype: float64
When I plot the data it has theese annoying horizontal lines because of weekends. Are there any simple way of simply removing the non-business days from the dataframe itself.
当我绘制数据时，由于周末，它有这些烦人的水平线。是否有任何简单的方法可以简单地从数据帧本身中删除非工作日。
Something like
就像是
df = df.BDays()
回答by Andy Hayden
One simple solution is to slice out the days not in Monday to Friday:
一个简单的解决方案是切出不在周一到周五的日子：
In [11]: s[s.index.dayofweek < 5]
Out[11]:
2016-05-02 00:00:00 4.780
2016-05-02 00:01:00 4.777
2016-05-02 00:02:00 4.780
2016-05-02 00:03:00 4.780
2016-05-02 00:04:00 4.780
Name: closeAsk, dtype: float64
Note: this doesn't take into account bank holidays etc.
注意：这不考虑银行假期等。
回答by Dave Babbitt
Pandas BDayjust ends up using .dayofweek<5like the chosen answer, but can be extended to account for bank holidays, etc.
PandasBDay最终会.dayofweek<5像选择的答案一样使用，但可以扩展到考虑银行假期等。
import pandas as pd
from pandas.tseries.offsets import BDay
isBusinessDay = BDay().onOffset
csv_path = 'C:\Python27\Lib\site-packages\bokeh\sampledata\daylight_warsaw_2013.csv'
dates_df = pd.read_csv(csv_path)
match_series = pd.to_datetime(dates_df['Date']).map(isBusinessDay)
dates_df[match_series]
回答by Henri Frits maarseveen
I am building a backtester for stock/FX trading and I also have these issue with days that are nan because that they are holidays or other non trading days.. you can download a financial calendar for the days that there is no trading and then you need to think about timezone and weekends.. etc..
我正在为股票/外汇交易构建一个回测器，我也有这些天数的问题，因为它们是假期或其他非交易日。需要考虑时区和周末……等等。
But the best solution is not to use date/time as the index for the candles or price. So do not connect your price data to a date/time but just to a counter of candles or prices .. you can use a second index for this.. so for calculations of MA or other technical lines dont use date/time .. if you look at Metatrader 4/5 it also doesnt use date/time but the index of the data is the candle number !!
但最好的解决方案是不要使用日期/时间作为蜡烛或价格的索引。因此，不要将您的价格数据连接到日期/时间，而只是连接到蜡烛或价格的计数器.. 您可以为此使用第二个索引.. 所以对于 MA 或其他技术线的计算不要使用日期/时间.. 如果你看看 Metatrader 4/5 它也不使用日期/时间，但数据的索引是蜡烛号！！
I think that you need to let go of the date-time for the price if you work with stock or FX data , of cause you can put them in a column of the data-frame but dont use it as the index This way you can avoid many problems
我认为如果您使用股票或外汇数据，您需要放弃价格的日期时间，因为您可以将它们放在数据框的一列中，但不要将其用作索引 这样您就可以避免很多问题
回答by oherbage
using workdays, you can count for holidays pretty easily
使用工作日，您可以很容易地计算假期
import workdays as wd
def drop_non_busdays(df, holidays=None):
if holidays is None:
holidays = []
start_date = df.index.to_list()[0].date()
end_date = df.index.to_list()[-1].date()
start_wd = wd.workday(wd.workday(start_date, -1, holidays), 1, holidays)
end_wd = wd.workday(wd.workday(end_date, 1, holidays), -1, holidays)
b_days = [start_wd]
while b_days[-1] < end_wd:
b_days.append(wd.workday(b_days[-1], 1, holidays))
valid = [i in b_days for i in df.index]
return df[valid]
