CODEWITHSUNDEEP
pythonpython-3.x
TanviP
TanviP

Reputation: 117

Python: random date generation

I want to generate random dates in the same format as the input, but no matter how many times I run the code, I want it to return the same random date, against each input value.

I'm reading from csv file to a data frame as follows:

in_df = pd.read_csv('file.txt',sep="\t", encoding='ISO-8859-1',usecols=['MY_DATE'])

Input dataframe:

MY_DATE 
0   2015-11-30 
1   2016-10-14 
2   2015-11-20

Expected output after 1st call to function:

MY_DATE 
0   2018-10-22 
1   2019-06-15 
2   2007-09-01

Expected output after 2nd call to function, for the same input:

MY_DATE 
0   2018-10-22 
1   2019-06-15 
2   2007-09-01

2015-11-30 results in, say, 2018-10-22 on first run, but doesn't result in the same date when I rerun it, even after setting the prop as global.

Here's my code snippet:

global prop
prop= random.random()
def gen_exp_dt(prop,start_dttm, end_dttm, format=None):
   stime = time.mktime(time.strptime(start_dttm,format))
   etime = time.mktime(time.strptime(end_dttm, format))
   rtime = stime + (prop * (etime - stime))
   return  time.strftime(format, time.localtime(rtime))

Any suggestions?

Upvotes: 1

Views: 15676

Answers (1)

user3483203
user3483203

Reputation: 51165

Use each date as your random seed. That way, each time you apply the function, you will get the same result for each date.

Next, generate a random integer between whatever range you want (I chose 1 and time.time()), and convert to a string with your desired format:

def random_date(seed):
    random.seed(seed)
    d = random.randint(1, int(time.time()))
    return datetime.fromtimestamp(d).strftime('%Y-%m-%d')

This will produce consistent results:

df
      MY_DATE
0  2015-11-30
1  2016-10-14
2  2015-11-20

df.MY_DATE.apply(random_date)

0    1978-07-06
1    1971-03-30
2    1998-06-05
Name: MY_DATE, dtype: object

df.MY_DATE.apply(random_date)

0    1978-07-06
1    1971-03-30
2    1998-06-05
Name: MY_DATE, dtype: object

Upvotes: 11

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