Pandas PeriodIndex function strftime() in Python
The Pandas PeriodIndex function strftime() used to convert index using specified date format. It returns an ndarray of formatted strings specified by formatted strings.
Syntax
 1 PeriodIndex.strftime(*args, **kwargs)
date_format : It is string that specifies the date format to convert. ie. '%Y-%m-%d'.
*args : It specifies the additional arguments to be passed to the function.
**kwargs : It specifies the additional keywords to be passed to the function.
strftime() function
 1 import pandas as pd
 2 
 3 pIndex = pd.period_range('2021-05-01 12:34:43', periods = 3, freq = 's')
 4 
 5 print('The original PeriodIndex object :')
 6 print(pIndex)
 7 
 8 res = pIndex.strftime('%B %d, %Y, %r')
 9 print('The string formatted time for each periods :')
 10 print(res)
In the above example, a PeriodIndex object is created using period_range() function by passing a date, periods and freq. A strftime() function is called by formatted string that format each period with specified formatted string and assign result to the variable that will be printed on console.
Output
 1 The original PeriodIndex object :
 2 PeriodIndex(['2021-05-01 12:34:43', '2021-05-01 12:34:44',
 3              '2021-05-01 12:34:45'],
 4             dtype='period[S]')
 5 			
 6 The string formatted time for each periods :
 7 Index(['May 01, 2021, 12:34:43 PM', 'May 01, 2021, 12:34:44 PM',
 8        'May 01, 2021, 12:34:45 PM'],
 9       dtype='object')
Format codes
 1 %a      Weekday, short version                          Wed	
 2 %A      Weekday, full version                           Wednesday	
 3 %w      Weekday as a number 0-6, 0 is Sunday            3	
 4 %d      Day of month 01-31                              31	
 5 %b      Month name, short version                       Dec	
 6 %B      Month name, full version                        December	
 7 %m      Month as a number 01-12                         12	
 8 %y      Year, short version, without century            18	
 9 %Y      Year, full version                              2018	
 10 %H      Hour 00-23                                      17	
 11 %I      Hour 00-12                                      05	
 12 %p      AM/PM                                           PM	
 13 %M      Minute 00-59                                    41	
 14 %S      Second 00-59                                    08	
 15 %f      Microsecond 000000-999999                       548513	
 16 %z      UTC offset                                      +0100	
 17 %Z      Timezone                                        CST	
 18 %j      Day number of year 001-366                      365	
 19 %U      Week of the year, Sunday as first day 00-53     52	
 20 %W      Week of the year, Monday as first day 00-53     52	
 21 %c      Local version of date and time                  Mon Dec 31 17:41:00 2018	
 22 %C      Century                                         20	
 23 %x      Local version of date                           12/31/18	
 24 %X      Local version of time                           17:41:00	
 25 %%      A % character                                   %	
 26 %G      ISO 8601 year                                   2018	
 27 %u      ISO 8601 weekday (1-7)                          1	
 28 %V      ISO 8601 weeknumber (01-53)                     01

Example 2

Example 2
 1 import pandas as pd
 2 
 3 dIndex = pd.date_range('2012-01-01 12:30:32',
 4 	periods = 3, freq = 'QE')
 5 pIndex = pd.PeriodIndex(dIndex)
 6 
 7 print('The original PeriodIndex object :')
 8 print(pIndex)
 9 
 10 res = pIndex.strftime('%B %d, %Y, %r')
 11 print('The string formatted time for each periods :')
 12 print(res)
In the above example, a PeriodIndex object is created using date_range() function by passing a date, periods and freq. A strftime() function is called by formatted string that format each period with specified formatted string and assign result to the variable that will be print.
Output
 1 The original PeriodIndex object :
 2 PeriodIndex(['2012Q1', '2012Q2', '2012Q3'], dtype='period[Q-DEC]')
 3 
 4 The string formatted time for each periods :
 5 Index(['March 31, 2012, 12:00:00 AM', 'June 30, 2012, 12:00:00 AM',
 6        'September 30, 2012, 12:00:00 AM'],
 7       dtype='object')
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