PANDAS：使用日期时间索引将熊猫系列 n 行切成过去

Question

upper_lower_bound returns 2 datetime indices from my dataframe. I only use one at a time and they have no relation to each other.

I want to get the max() value of the previous 6 rows data from a dataframe highP but I get an error if I try to subtract 6 from it. dt.timedelta(6) subtracts 6 days from the df but there are missing days in the df so it doesn't provide the correct answer.

How can I slice highP so that it gives me the previous six values in that series for eg.

highP.loc[i - 6: i].max() given that i is a datetime index?

any help would be greatly appreciated!

upper_lower_bound = df[(isoHL['IH'] >= 1) | (isoHL['IL'] >= 1)].index[-3:-1]

if isoHL.loc[upper_lower_bound[-1]]['IH'] == 1 and isoHL.loc[upper_lower_bound[-1]]['IL'] == 0:
    upper_bound = highP.loc[upper_lower_bound[-1] - dt.timedelta(6):upper_lower_bound[-1]].max()
else:
    pass

Answer 1

How about sorting by time and taking last elements with tail? For generality, I will use notations with:

• dataframe = df
• time column = t
• value column = val
• certain location = i

first we obtain a df of sample with time < time at i :

before_df = df[df['t'] <= df.loc[i, 't']]

then we sort by time:

before_df = before_df.sort_values(by=['t'])

then we take last five with 'tail':

five_before = before_df.tail(5)

then we max over value:

val = five_before['val'].max()

Does this solve your question?

`