How to convert string datetime of a Dataframe into Datetime

Question

I am working with currently with a csv file that contains datetimes and timestamps. The dataframe look like this:

print(df[:10])

  [0 '2019-10-10 21:59:17.074007' '2015-10-13 00:55:55.544607'
 '2017-05-24 06:00:15.959202' '2016-12-07 09:01:04.729686'
 '2019-05-29 11:16:44.130063' '2017-01-19 16:06:37.625964'
 '2018-04-07 19:42:43.708620' '2016-06-28 03:13:58.266977'
 '2015-03-21 00:03:07.704446']

and now I want to convert those strings into datetime and find the earliest date out of it. I don't have much experience in datetime dataframes so I am not sure how to do it. Any suggestions?

That looks like a list. What is the output of: type(df)

tdy
– tdy

2021-04-05 11:20:04 +00:00
Commented Apr 5, 2021 at 11:20 — tdy
– tdy, Commented Apr 5, 2021 at 11:20

perl · Accepted Answer · 2021-04-05 11:28:12Z

2

You can convert strings to_datetime, then take min:

dates = ['2019-10-10 21:59:17.074007', '2015-10-13 00:55:55.544607',
 '2017-05-24 06:00:15.959202', '2016-12-07 09:01:04.729686',
 '2019-05-29 11:16:44.130063', '2017-01-19 16:06:37.625964',
 '2018-04-07 19:42:43.708620', '2016-06-28 03:13:58.266977',
 '2015-03-21 00:03:07.704446']

pd.to_datetime(dates).min()

Output:

Timestamp('2015-03-21 00:03:07.704446')

Update

If you want to do it across all columns of the dataframe:

df.apply(pd.to_datetime).min().min()

edited Apr 5, 2021 at 11:28

answered Apr 5, 2021 at 11:19

perl

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3 Comments

Skarl001 Over a year ago

I can't use that because I have many values in the dataframe (I think 100 columns and rows) and to_datetime does not work for a reason

perl Over a year ago

Please see the update, it basically applies to_datetime column-wise, then takes min for each column, and then takes min among column minimums

perl Over a year ago

And if only some columns of your dataframe are dates in string format, then you can, of course, do df[['col1', 'col2']].apply(pd.to_datetime).min().min() (assuming the dates are in col1 and col2

Hook · Accepted Answer · 2021-04-05 11:23:13Z

1

Lets call the list you mentioned l, you can iterate on it and parse dates using datetime.strptime, aggregate them in a new list and return the earliest:

from datetime import datetime

parsed_dates = []

for d in l:
    parsed_dates.append(datetime.strptime(d, "%Y-%m-%d %H:%M:%S.%f"))
print(min(parsed_dates))

answered Apr 5, 2021 at 11:23

Hook

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Comments

Anurag Dabas · Accepted Answer · 2021-04-05 11:29:06Z

1

Convert these value to datetime by using to_datetime() method:

df=pd.to_datetime(df,errors='coerce')

Now find earliest date by using min() method:

earliest_date=df.min()

OR you can also find earliest date by using nsmallest() method(This works on Series):

earliest_date=df.nsmallest(1)

edited Apr 5, 2021 at 11:29

answered Apr 5, 2021 at 11:20

Anurag Dabas

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