Generating random dates within a given range in pandas

This is a self-answered post. A common problem is to randomly generate dates between a given start and end date.

There are two cases to consider:

1. random dates with a time component, and
2. random dates without time

For example, given some start date 2015-01-01 and an end date 2018-01-01, how can I sample N random dates between this range using pandas?

Is converting to the unix timestamp acceptable?

def random_dates(start, end, n=10):

    start_u = start.value//10**9
    end_u = end.value//10**9

    return pd.to_datetime(np.random.randint(start_u, end_u, n), unit='s')

Sample run:

start = pd.to_datetime('2015-01-01')
end = pd.to_datetime('2018-01-01')
random_dates(start, end)

DatetimeIndex(['2016-10-08 07:34:13', '2015-11-15 06:12:48',
               '2015-01-24 10:11:04', '2015-03-26 16:23:53',
               '2017-04-01 00:38:21', '2015-05-15 03:47:54',
               '2015-06-24 07:32:32', '2015-11-10 20:39:36',
               '2016-07-25 05:48:09', '2015-03-19 16:05:19'],
              dtype='datetime64[ns]', freq=None)

EDIT:

As per the comment by @smci, I wrote a function to accommodate both 1 and 2 with a little explanation inside the function itself.

def random_datetimes_or_dates(start, end, out_format='datetime', n=10): 

    '''   
    unix timestamp is in ns by default. 
    I divide the unix time value by 10**9 to make it seconds (or 24*60*60*10**9 to make it days).
    The corresponding unit variable is passed to the pd.to_datetime function. 
    Values for the (divide_by, unit) pair to select is defined by the out_format parameter.
    for 1 -> out_format='datetime'
    for 2 -> out_format=anything else
    '''
    (divide_by, unit) = (10**9, 's') if out_format=='datetime' else (24*60*60*10**9, 'D')

    start_u = start.value//divide_by
    end_u = end.value//divide_by

    return pd.to_datetime(np.random.randint(start_u, end_u, n), unit=unit) 

Sample run:

random_datetimes_or_dates(start, end, out_format='datetime')

DatetimeIndex(['2017-01-30 05:14:27', '2016-10-18 21:17:16',
               '2016-10-20 08:38:02', '2015-09-02 00:03:08',
               '2015-06-04 02:38:12', '2016-02-19 05:22:01',


                  '2015-11-06 10:37:10', '2017-12-17 03:26:02',
                   '2017-11-20 06:51:32', '2016-01-02 02:48:03'],
                  dtype='datetime64[ns]', freq=None)

random_datetimes_or_dates(start, end, out_format='not datetime')

DatetimeIndex(['2017-05-10', '2017-12-31', '2017-11-10', '2015-05-02',
               '2016-04-11', '2015-11-27', '2015-03-29', '2017-05-21',
               '2015-05-11', '2017-02-08'],
              dtype='datetime64[ns]', freq=None)

np.random.randn + to_timedelta

This addresses Case (1). You can do this by generating a random array of timedelta objects and adding them to your start date.

def random_dates(start, end, n, unit='D', seed=None):
    if not seed:  # from piR's answer
        np.random.seed(0)

    ndays = (end - start).days + 1
    return pd.to_timedelta(np.random.rand(n) * ndays, unit=unit) + start

>>> np.random.seed(0)
>>> start = pd.to_datetime('2015-01-01')
>>> end = pd.to_datetime('2018-01-01')
>>> random_dates(start, end, 10)
DatetimeIndex([   '2016-08-25 01:09:42.969600',
                  '2017-02-23 13:30:20.304000',
                  '2016-10-23 05:33:15.033600',
               '2016-08-20 17:41:04.012799999',
               '2016-04-09 17:59:00.815999999',
                  '2016-12-09 13:06:00.748800',
                  '2016-04-25 00:47:45.974400',
                  '2017-09-05 06:35:58.444800',
                  '2017-11-23 03:18:47.347200',
                  '2016-02-25 15:14:53.894400'],
              dtype='datetime64[ns]', freq=None)

This will generate dates with a time component as well.

Sadly, rand does not support a replace=False, so if you want unique dates, you'll need a two-step process of 1) generate the non-unique days component, and 2) generate the unique seconds/milliseconds component, then add the two together.

np.random.randint + to_timedelta

This addresses Case (2). You can modify random_dates above to generate random integers instead of random floats:

def random_dates2(start, end, n, unit='D', seed=None):
    if not seed:  # from piR's answer
        np.random.seed(0)

    ndays = (end - start).days + 1
    return start + pd.to_timedelta(
        np.random.randint(0, ndays, n), unit=unit
    )

>>> random_dates2(start, end, 10)
DatetimeIndex(['2016-11-15', '2016-07-13', '2017-04-15', '2017-02-02',
               '2017-10-30', '2015-10-05', '2016-08-22', '2017-12-30',
               '2016-08-23', '2015-11-11'],
              dtype='datetime64[ns]', freq=None)

To generate dates with other frequencies, the functions above can be called with a different value for unit. Additionally, you can add a parameter freq and tweak your function call as needed.

If you want unique random dates, you can use np.random.choice with replace=False:

def random_dates2_unique(start, end, n, unit='D', seed=None):
    if not seed:  # from piR's answer
        np.random.seed(0)

    ndays = (end - start).days + 1
    return start + pd.to_timedelta(
        np.random.choice(ndays, n, replace=False), unit=unit
    )

Performance

Going to benchmark just the methods that address Case (1), since Case (2) is really a special case which any method can get to using dt.floor.

Functions

def cs(start, end, n):
    ndays = (end - start).days + 1
    return pd.to_timedelta(np.random.rand(n) * ndays, unit='D') + start

def akilat90(start, end, n):
    start_u = start.value//10**9
    end_u = end.value//10**9

    return pd.to_datetime(np.random.randint(start_u, end_u, n), unit='s')

def piR(start, end, n):
    dr = pd.date_range(start, end, freq='H') # can't get better than this :-(
    return pd.to_datetime(np.sort(np.random.choice(dr, n, replace=False)))

def piR2(start, end, n):
    dr = pd.date_range(start, end, freq='H')
    a = np.arange(len(dr))
    b = np.sort(np.random.permutation(a)[:n])
    return dr[b]

Benchmarking Code

from timeit import timeit

import pandas as pd
import matplotlib.pyplot as plt

res = pd.DataFrame(
       index=['cs', 'akilat90', 'piR', 'piR2'],
       columns=[10, 20, 50, 100, 200, 500, 1000, 2000, 5000],
       dtype=float
)

for f in res.index: 
    for c in res.columns:
        np.random.seed(0)

        start = pd.to_datetime('2015-01-01')
        end = pd.to_datetime('2018-01-01')

        stmt = '{}(start, end, c)'.format(f)
        setp = 'from __main__ import start, end, c, {}'.format(f)
        res.at[f, c] = timeit(stmt, setp, number=30)

ax = res.div(res.min()).T.plot(loglog=True) 
ax.set_xlabel("N"); 
ax.set_ylabel("time (relative)");

plt.show()

We can speed up @akilat90's approach about twofold (in @coldspeed's benchmark) by using the fact that datetime64 is just a rebranded int64 hence we can view-cast:

def pp(start, end, n):
    start_u = start.value//10**9
    end_u = end.value//10**9

    return pd.DatetimeIndex((10**9*np.random.randint(start_u, end_u, n, dtype=np.int64)).view('M8[ns]'))