[英]Get mean of last N weekdays for pandas dataframe
Assume my data is daily counts and has as its index a DateTimeIndex column. 假设我的数据是每日计数,并将DateTimeIndex列作为其索引。 Is there a way to get the average of the past n weekdays?
有没有办法获得过去n个工作日的平均值? For instance, if the date is Sunday August 15th, I'd like to get mean of counts on (sunday august 8th, sunday august 1st, ...).
例如,如果日期是8月15日星期日,我想得到统计数字(星期日8月8日,星期日8月1日,......)。
I started using pandas yesterday, so here's what I've brute forced. 我昨天开始使用大熊猫,所以这就是我的强奸。
# df is a dataframe with an DateTimeIndex
# brute force for count last n weekdays, wherelnwd = last n weekdays
def lnwd(n=1):
lnwd, tmp = df.shift(7), df.shift(7) # count last weekday
for i in xrange(n-1):
tmp = tmp.shift(7)
lnwd += tmp
lnwd = lnwd/n # average
return lnwd
There has to be a one liner? 必须有一个班轮? Is there a way to use
apply()
(without passing a function that has a for loop? since n
is variable) or some form of groupby
? 有没有办法使用
apply()
(不传递具有for循环的函数?因为n
是可变的)或某种形式的groupby
? For instance, the way to find the mean of all data on each weekday is: 例如,在每个工作日查找所有数据的平均值的方法是:
df.groupby(lambda x: x.dayofweek).mean() # mean of each MTWHFSS
I think you are looking for a rolling apply (rolling mean in this case)? 我认为你正在寻找滚动申请(在这种情况下滚动均值)? See the docs: http://pandas.pydata.org/pandas-docs/stable/computation.html#moving-rolling-statistics-moments .
请参阅文档: http : //pandas.pydata.org/pandas-docs/stable/computation.html#moving-rolling-statistics-moments 。 But then applied for each weekday seperately, this can be achieved by combining
rolling_mean
with grouping on the weekday with groupby
. 但随后在每个工作日分别申请,这可以通过将
rolling_mean
与工作日的分组与groupby
相结合来实现。
This should give somethin like (with a series s
): 这应该给某些东西(有系列
s
):
s.groupby(s.index.weekday).transform(lambda x: pd.rolling_mean(x, window=n))
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