Convert a Particular Dataframe Column Into Customized Date Or Time - pandas

Question

As you can see a Date & Time Column are being saved in this CSV File. Now what problem is that the date & time are in format of something like - 30-1-2022 & 20:08:00
But i want it to look something like 30th Jan 22 and 8:08 PM
Any code for that ?
import requests
import pandas as pd
from datetime import datetime
from datetime import date
currentd = date.today()
s = requests.Session()
headers = {'user-agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36'}
url = 'https://www.nseindia.com/'
step = s.get(url,headers=headers)
today = datetime.now().strftime('%d-%m-%Y')
api_url = f'https://www.nseindia.com/api/corporate-announcements?index=equities&from_date={today}&to_date={today}'
resp = s.get(api_url,headers=headers).json()
result = pd.DataFrame(resp)
result.drop(['difference', 'dt','exchdisstime','csvName','old_new','orgid','seq_id','sm_isin','bflag','symbol','sort_date'], axis = 1, inplace = True)
result.rename(columns = {'an_dt':'DateandTime', 'attchmntFile':'Source','attchmntText':'Topic','desc':'Type','smIndustry':'Sector','sm_name':'Company Name'}, inplace = True)
result[['Date','Time']] = result.DateandTime.str.split(expand=True)
result.drop(['DateandTime'], axis = 1, inplace = True)
result.to_csv( ( str(currentd.day) +'-'+str(currentd.month) +'-'+'CA.csv'), index=True)
print('Saved the CSV File')

Answer 1

Try creating a temporary column:
result['Full_date']=pd.to_datetime(result['Date']+' '+result['Time'])
Then format 'Date' and 'Time'
result['Date']=result['Full_date'].dt.strftime('%b %d, %Y')
result['Time']=result['Full_date'].dt.strftime('%R' '%p')

Answer 2

Try this:
# Remove comment if needed
# import locale
# locale.setlocale(locale.LC_TIME, 'C')
# https://stackoverflow.com/a/16671271
def ord(n):
return str(n)+("th" if 4<=n%100<=20 else {1:"st",2:"nd",3:"rd"}.get(n%10, "th"))
result['Date'] = pd.to_datetime(result['Date'], format='%d-%b-%Y')
result['Date'] = result['Date'].dt.day.map(ord) + result['Date'].dt.strftime(' %b %Y')
result['Time'] = pd.to_datetime(result['Time']).dt.strftime('%-H:%M %p')
# Now you can export
Output:
>>> result[['Date', 'Time']]
Date Time
0 30th Jan 2022 21:07 PM
1 30th Jan 2022 20:57 PM
2 30th Jan 2022 19:40 PM
3 30th Jan 2022 18:55 PM
4 30th Jan 2022 18:53 PM
5 30th Jan 2022 18:09 PM
6 30th Jan 2022 17:44 PM
7 30th Jan 2022 16:01 PM
8 30th Jan 2022 15:21 PM
9 30th Jan 2022 15:16 PM
10 30th Jan 2022 15:10 PM
11 30th Jan 2022 15:06 PM
12 30th Jan 2022 14:29 PM
13 30th Jan 2022 14:15 PM
14 30th Jan 2022 13:41 PM
15 30th Jan 2022 12:20 PM
16 30th Jan 2022 12:09 PM
17 30th Jan 2022 12:07 PM
18 30th Jan 2022 10:58 AM
19 30th Jan 2022 10:42 AM
20 30th Jan 2022 10:40 AM
21 30th Jan 2022 10:39 AM
22 30th Jan 2022 10:06 AM
23 30th Jan 2022 9:39 AM
24 30th Jan 2022 9:36 AM
25 30th Jan 2022 9:25 AM
26 30th Jan 2022 8:43 AM
27 30th Jan 2022 1:00 AM
28 30th Jan 2022 0:59 AM
29 30th Jan 2022 0:13 AM