Python 如何接受仅从最近的星期一开始的数据
我有以下代码:Python 如何接受仅从最近的星期一开始的数据,python,pandas,datetime,Python,Pandas,Datetime,我有以下代码: import pandas as pd from pandas import datetime from pandas import DataFrame as df import matplotlib from pandas_datareader import data as web import matplotlib.pyplot as plt import datetime import numpy as np stock = '^GSPC' start = datetim
import pandas as pd
from pandas import datetime
from pandas import DataFrame as df
import matplotlib
from pandas_datareader import data as web
import matplotlib.pyplot as plt
import datetime
import numpy as np
stock = '^GSPC'
start = datetime.date(2000,1,1)
end = datetime.date.today()
data = web.DataReader(stock, 'yahoo',start, end)
data.index = pd.to_datetime(data.index, format ='%Y-%m-%d')
data['Day Name'] = data.index.weekday_name
data.set_index('day',append=True,inplace=True)
data.set_index('Day Name', append=True, inplace=True)
data['pct_day']= data['Adj Close'].pct_change()
df = data.groupby(['Day Name']).mean()
df = df.drop( index=['Saturday','Sunday'])
df = df.reindex(['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'])
我需要做的是,无论输入的日期是什么,数据应该只接受最近一个星期一的数据,而不接受之前的数据。在此感谢您的帮助。您可以在第一次
星期一
之前使用删除值,并通过以下方式获取不等于0
的所有行:
另一个想法是使用第一个索引值并传递给,只需要数据中至少有一个Monday
:
data['Day Name'] = data.index.weekday_name
data = data.loc[data['Day Name'].eq('Monday').idxmax():]
data.set_index('day',append=True,inplace=True)
样本:
rng = pd.date_range('2017-04-01', periods=10)
data = pd.DataFrame({'a': range(10)}, index=rng)
data['Day Name'] = data.index.weekday_name
data = data[data['Day Name'].eq('Monday').cumsum().ne(0)].copy()
print (data)
a Day Name
2017-04-03 2 Monday
2017-04-04 3 Tuesday
2017-04-05 4 Wednesday
2017-04-06 5 Thursday
2017-04-07 6 Friday
2017-04-08 7 Saturday
2017-04-09 8 Sunday
2017-04-10 9 Monday
rng = pd.date_range('2017-04-01', periods=10)
data = pd.DataFrame({'a': range(10)}, index=rng)
data['Day Name'] = data.index.weekday_name
data = data[data['Day Name'].eq('Monday').cumsum().ne(0)].copy()
print (data)
a Day Name
2017-04-03 2 Monday
2017-04-04 3 Tuesday
2017-04-05 4 Wednesday
2017-04-06 5 Thursday
2017-04-07 6 Friday
2017-04-08 7 Saturday
2017-04-09 8 Sunday
2017-04-10 9 Monday