Pandas Date and time functions to manage Datetime

Current Date and time

import pandas as pd 
print(pd.to_datetime('now'))

Output

2022-06-10 12:13:23.001423

Any Date and time

present_time=pd.to_datetime('12252020:233445',format='%m%d%Y:%H%M%S')

Using any date and time

import pandas as pd 
tm=pd.to_datetime('2020-12-29 18:15:05')
# Use day,month, year, hour , second , dayofweek, day_name()
print(tm.minute)

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King'],
  'dt_start':['2020-12-28 18:15:05', '2021-01-31 23:15:45',
   '2021-02-14 04:15:37', '2021-05-17 09:15:26','2022-06-20 13:45:54']
         }
my_data = pd.DataFrame(data=my_dict)
my_data['dt_start'] = pd.to_datetime(my_data['dt_start'])

print(my_data)

Managing Date

to_datetime()	Converting to Datetime
to_timedelta()	Convert argument to timedelta for calculation
date_range()	Generating range of Date and time
period_range()	Generating fixed frequency Datetime index
strftime()	Generating Date and time using format
timedelta64()	Date and time calculations using different Units
Timestamp()	Using Timestamp from Date and time inputs
Dateoffset()	Adding or Subtracting date parts
resample()	Resampling datetime index using different frequencies

Exercise datetime 3-1 Exercise datetime 3-2 Exercise datetime 3-3

Date & time offset Aliases

Alias	Description
B	business day frequency
C	custom business day frequency
D	calendar day frequency
W	weekly frequency
M	month end frequency
SM	semi-month end frequency (15th and end of month)
BM	business month end frequency
CBM	custom business month end frequency
MS	month start frequency
SMS	semi-month start frequency (1st and 15th)
BMS	business month start frequency
CBMS	custom business month start frequency
Q	quarter end frequency
BQ	business quarter end frequency
QS	quarter start frequency
BQS	business quarter start frequency
A,Y	year end frequency
BA,BY	business year end frequency
AS,YS	year start frequency
BAS, BYS	business year start frequency
BH	business hour frequency
H	hourly frequency
T,min	minutely frequency
S	secondly frequency
L,ms	milliseconds
U,us	microseconds
N	nanoseconds

Pandas Python- Tutorials

Subhendu Mohapatra

Author & Instructor at plus2net

I write and maintain practical tutorials on Python, PHP, SQL, JavaScript, HTML, jQuery, and web development at plus2net. The tutorials focus on clear explanations, working examples, and code that readers can test and adapt while learning.

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