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Convert string of dd-MON-yy to date in Python

I have a string in dd-MON-yy format. While converting to date in python, its is causing issue since the year is in tow digits.
datetime.datetime.strptime('17-JUN-03', '%d-%m-%y')
The error is,
ValueError: time data '17-JUN-03' does not match format '%d-%m-%y'

Try this:
import datetime
print(datetime.datetime.strptime('17-JUN-03', '%d-%b-%y'))
Result:
2003-06-17 00:00:00
Datetime format codes

Convert ISO 8061 datetime to timestamp in python

I have a string with a date in the format: 2021-03-12T14:45:34.000Z
I would like to convert it to a standard format as this one: 12-Mar-2021 14:45:34
I tried using:
print(datetime.datetime.strptime("2021-03-12T14:45:34.000Z", "%Y-%m-%dT%H:%M:%S%fZ"))
but I get the error:
ValueError: time data '2021-03-12T14:45:34.000Z' does not match format
'%Y-%m-%dT%H:%M:%S%fZ'
How can I solve it?

You need to get as datetime then convert to forrmat as you like:
import datetime
date = '2021-03-12T14:45:34.000Z'
datetime.datetime.strptime(date, "%Y-%m-%dT%H:%M:%S.%fZ"
).strftime('%d-%b-%Y %H:%M:%S')
Output:
'12-Mar-2021 14:45:34'

You are missing a . in your format string. The correct format string is
"%Y-%m-%dT%H:%M:%S.%fZ"
Notice the . after %S and before %fZ.

Dataframe has string column with date values in different formats. how to convert entire column data to single date format?

converting string to datetime in python pandas with multiple date formats in a column.
Dataframe startdate has date values in 'mm-dd-YYYY hh:mm' format and 'mm/dd/YYYY hh:mm' format.
I have used following query
df['START_DATE*'] = pd.to_datetime(df['START_DATE*'], format= '%m-%d-%Y hh:mm')
START_DATE* is the column name .giving following
time data '01-01-2016 21:11' does not match format '%m-%d-%Y hh:mm' (match)

hh:mm does not mean hour:minute. Try:
df['START_DATE*'] = pd.to_datetime(df['START_DATE*'], format= '%m-%d-%Y %H:%M')

Converting pandas Column to datetime

I am trying to convert a pandas column to datetime. This is my error message.
ValueError: time data '01-JUN-17 00:00:00' does not match format
'%d-%b-%y %H.%M.%S' (match)
This is my code :
df['dayofservice'] = pd.to_datetime(df['dayofservice'], format = '%d-%b-%y %H.%M.%S')
I have read this documentation to ensure my format is correct : https://docs.python.org/2/library/datetime.html#strftime-and-strptime-behavior
It's still not working for me.

pandas is man/woman enough to parse this without a format field:
In[90]:
pd.to_datetime('01-JUN-17 00:00:00')
Out[90]: Timestamp('2017-06-01 00:00:00')
So this should work:
df['dayofservice'] = pd.to_datetime(df['dayofservice'])

Change . to : in times like:
df['dayofservice'] = pd.to_datetime(df['dayofservice'], format = '%d-%b-%y %H:%M:%S')

Extract Date from excel and append it in a list using python

I have an column in excel which has dates in the format ''17-12-2015 19:35". How can I extract the first 2 digits as integers and append it to a list? In this case I need to extract 17 and append it to a list. Can it be done using pandas also?
Code thus far:
import pandas as pd
Location = r'F:\Analytics Materials\files\paymenttransactions.csv'
df = pd.read_csv(Location)
time = df['Creation Date'].tolist()
print (time)

You could extract the day of each timestamp like
from datetime import datetime
import pandas as pd
location = r'F:\Analytics Materials\files\paymenttransactions.csv'
df = pd.read_csv(location)
timestamps = df['Creation Date'].tolist()
dates = [datetime.strptime(timestamp, '%d-%m-%Y %H:%M') for timestamp in timestamps]
days = [date.strftime('%d') for date in dates]
print(days)
The '%d-%m-%Y %H:%M'and '%d' bits are format specififers, that describe how your timestamp is formatted. See e.g. here for a complete list of directives.
datetime.strptime parses a string into a datetimeobject using such a specifier. dateswill thus hold a list of datetime instances instead of strings.
datetime.strftime does the opposite: It turns a datetime object into string, again using a format specifier. %d simply instructs strftime to only output the day of a date.