自动填充 Pandas 中的日期时间，按先前的增量

Question

Given previous datetime values in a Pandas DataFrame--either as an index or as values in a column--is there a way to "autofill" remaining time increments based on the previous fixed increments?

For example, given:

import pandas as pd
import numpy as np    
df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
                      index = [pd.Timestamp('20130101 09:00:00'),
                               pd.Timestamp('20130101 09:00:05'),
                               pd.Timestamp('20130101 09:00:10'),
                               np.nan,
                               np.nan])

I would like to apply a function to yield:

	B
2013-01-01 09:00:00	0.0
2013-01-01 09:00:05	1.0
2013-01-01 09:00:10	2.0
2013-01-01 09:00:15	NaN
2013-01-01 09:00:20	4.0

Where I have missing timesteps for my last two data points. Here, timesteps are fixed in 5 second increments.

This will be for thousands of rows. While I might reset_index and then create a function to apply to each row, this seems cumbersome. Is there a slick or built-in way to do this that I'm not finding?

Answer 1

This solution might work for you,but also use reset_index() fuction.

new_dateindex=pd.Series(pd.date_range(start=pd.Timestamp('20130101 09:00:00'),periods=1000,freq='5S'),name='Date')

#'periods=1000' can change to 'periods=len(df.index)'

df.reset_index().join(new_dateindex,how='right')

Answer 2

Assuming the first index value is a valid datetime and all the values are spaced 5s apart, you could do the following:

df.index = pd.date_range(df.index[0], periods=len(df), freq='5s')
>>> df
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:05  1.0
2013-01-01 09:00:10  2.0
2013-01-01 09:00:15  NaN
2013-01-01 09:00:20  4.0