Generate A Random List Of N Dates In The Iso 8601 Format Within A Range In Python

I want to generate a random list of dates in the iso8601 format within the range from 2019-01-01 to 2019-12-31 n times. from datetime import date start_date = date(2019,1,1) end_da

Solution 1:

You can use random.sample to sample without replacement or random.choices to sample with replacement after generating a list of all the dates in the range.

If you don't want to store the list you could also generate N random numbers from 1 through 365, then convert those to the appropriate dates.

import random

from datetime import date, timedelta

end_date =date(2019, 12, 31)
current_date=date(2019, 1, 1)
n =3

step = timedelta(days=1)

dates = [current_date]
while current_date!= end_date:
    current_date+= step
    dates.append(current_date)

random_dates = random.choices(dates, k=n)
print([d.isoformat() for d in random_dates])

Solution 2:

You can do something like this

import datetime
import random

# startdate
start_date = datetime.date(2019, 1, 1)

# enddate
end_date = datetime.date(2019, 12, 31)

time_between_dates = end_date - start_date
days_between_dates = time_between_dates.days
#workload in days

random.seed(a=None)

random_number_of_days = random.randrange(days_between_dates)
random_date = start_date + datetime.timedelta(days=random_number_of_days)
print(str(random_date))

Which gave the following result when I ran it

2019-06-07

A similar question has been asked here Python - Generate random dates to create Gantt sequenced tasks?

Most of the code is from there except the last loop

Solution 3:

I create a dataframe with an datetimeindex with two iso8601 date values. I then resample the dataframe index to every 30Minute intervals then randomly choose 3 items from the dataframe.

df=pd.DataFrame({'timestamp':['2019-01-01T00:00:00.000Z','2019-12-31T23:59:59.300Z']})
df['timestamp']=df['timestamp'].apply(lambda timestamp: datetime.strptime(timestamp, '%Y-%m-%dT%H:%M:%S.%f%z'))
print(df['timestamp'])
df=df.set_index('timestamp')
dates = df.resample('30Min').max().dropna()
#print(dates)
random_dates = random.choices(dates.index, k=3)
print(random_dates)

output:

[Timestamp('2019-08-29 16:30:00+0000', tz='UTC', freq='30T'), Timestamp('2019-11-09 03:30:00+0000', tz='UTC', freq='30T'), Timestamp('2019-08-02 12:00:00+0000', tz='UTC', freq='30T')]