Find closest row of DataFrame to given time in Pandas

I think you can try DatetimeIndex.asof to find the most recent label up to and including the input. Then use the returned datetime to select the appropriate row. If you only need values for a particular column, Series.asof exists and combines the two steps above into one.

This assumes you want the closest datetime. If you don't care about the date and just want the same time every day, use at_time in DataFrame.

Follow up:

Edit: false alarm, I had an older version locally. The latest on master should work with np.abs.

In [10]: np.abs(df.time - image_time)
Out[10]: 
0    27 days, 13:39:02
1    26 days, 13:39:02
2    25 days, 13:39:02
3    24 days, 13:39:02
4    23 days, 13:39:02
5    22 days, 13:39:02

Also just to clarify:

aeronet.index - image_time doesn't work because subtraction on Index is a set difference (back in the day Index used to be constrained to be unique).

I was confronting the same problem today. I wanted a function able to give me the closest value prior a given timestamp. Here is the function I got:

def get_nearest_past(data, timestamp):
    index = data.index.get_loc(timestamp,"ffill")
    return data.iloc[index]

In the case that you need the global closest (and not the closest before as in my case), you can use:

def get_nearest(data, timestamp):
    index = data.index.get_loc(timestamp,"nearest")
    return data.iloc[index]

You can find more information in the get_loc documentation.

This simple method will return the (integer index of the) TimeSeriesIndex entry closest to a given datetime object. There's no need to copy the index to a regular column - simply use the .to_pydatetime method instead.

import numpy as np

i = np.argmin(np.abs(df.index.to_pydatetime() - image_time))

Then you simply use the DataFrame's .iloc indexer:

df.iloc[i]

Here's a function to do this:

def fcl(df, dtObj):
    return df.iloc[np.argmin(np.abs(df.index.to_pydatetime() - dtObj))]

You can then further filter seamlessly, e.g.

fcl(df, dtObj)['column']