Pandas: Timestamp index rounding to the nearest 5th minute

The round_to_5min(t) solution using timedelta arithmetic is correct but complicated and very slow. Instead make use of the nice Timstamp in pandas:

import numpy as np
import pandas as pd

ns5min=5*60*1000000000   # 5 minutes in nanoseconds 
pd.to_datetime(((df.index.astype(np.int64) // ns5min + 1 ) * ns5min))

Let's compare the speed:

rng = pd.date_range('1/1/2014', '1/2/2014', freq='S')

print len(rng)
# 86401

# ipython %timeit 
%timeit pd.to_datetime(((rng.astype(np.int64) // ns5min + 1 ) * ns5min))
# 1000 loops, best of 3: 1.01 ms per loop

%timeit rng.map(round_to_5min)
# 1 loops, best of 3: 1.03 s per loop

Just about 1000 times faster!

You can try something like this:

def round_to_5min(t):
    delta = datetime.timedelta(minutes=t.minute%5, 
                               seconds=t.second, 
                               microseconds=t.microsecond)
    t -= delta
    if delta > datetime.timedelta(0):
        t += datetime.timedelta(minutes=5)
    return t

df['new_col'] = df.index.map(round_to_5min)

One could easily use the round function of pandas

df["timestamp_column"].dt.round("5min")

Check here for more details