Improper parsing of String to DateTime in pandas

Question

I have a dataframe in pandas having two fields:

UsageStartDate        UsageEndDate

5/1/2020 12:00:00 AM  5/1/2020 11:59:59 PM

I am trying to convert them to 24-hour format.

I am using the following:

df['UsageStartDate'] = pd.to_datetime(df['UsageStartDate'], format='%m/%d/%Y %I:%M:%S %p')
df['UsageEndDate'] = pd.to_datetime(df['UsageEndDate'], format='%m/%d/%Y %I:%M:%S %p')

The UsageEndDate field gives proper output: 2020-05-01 23:59:59

However, the UsageStartDate gives: 2020-05-01

I also tried to parse the date during reading of the csv file:

df = pd.read_csv('./data/sample.csv', parse_dates=['UsageStartDate', 'UsageEndDate'])

It returned the same result as above.

Kindly help.

Dave · Accepted Answer · 2020-05-14 13:41:46Z

1

Pandas will display fields without trailing zeros, as an aesthetic choice. Your actual Timestamp value is correct:

df['UsageStartDate'] + pd.Timedelta(seconds=1)

0   2020-05-01 00:00:01
Name: UsageStartDate, dtype: datetime64[ns]

If you want the full value as a string, you can get it with the Timestamp string formatter, strftime():

df['UsageStartDate'].dt.strftime('%m/%d/%Y %I:%M:%S %p')

0    05/01/2020 12:00:00 AM
Name: UsageStartDate, dtype: object # <- Note that this is now object type

answered May 14, 2020 at 13:41

Dave

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