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change YYYYDDMM to YYYYMMDD in python

I have a df with dates in a column converted to a datetime. the current format is YYYYDDMM. I need this converted to YYYYMMDD. I tried the below code but it does not change the format and still gives me YYYYDDMM. the end goal is to subtract 1 business day from the effective date but the format needs to be in YYYYMMDD to do this otherwise it subtracts 1 day from the M and not D. can someone help?
filtered_df['Effective Date'] = pd.to_datetime(filtered_df['Effective Date'])
# Effective Date = 20220408 (4th Aug 2022 for clarity)
filtered_df['Effective Date new'] = filtered_df['Effective Date'].dt.strftime("%Y%m%d")
# Effective Date new = 20220408
desired output -- > Effective Date new = 20220804

By default, .to_datetime will interpret the input YYYYDDMM as YYYYMMDD, and therefore print the same thing with %Y%m%d as the format. You can fix this and make it properly parse days in the month greater than 12 by adding the dayfirst keyword argument.
filtered_df['Effective Date'] = pd.to_datetime(filtered_df['Effective Date'], dayfirst=True)

I like to use the datetime library for this purpose. You can use strptime to convert a string into the datetime object and strftime to convert your datetime object to the new string.
from datetime import datetime
def change_date(row):
row["Effective Date new"] = datetime.strptime(row["Effective Date"], "%Y%d%m").strftime("%Y%m%d")
return row
df2 = df.apply(change_date, axis=1)
The output df2 will have Effective Date new as your new column.

extract date, month and year from string in python

I have this column where the string has date, month, year and also time information. I need to take the date, month and year only.
There is no space in the string.
The string is on this format:
date
Tuesday,August22022-03:30PMWIB
Monday,July252022-09:33PMWIB
Friday,January82022-09:33PMWIB
and I expect to get:
date
2022-08-02
2022-07-25
2022-01-08
How can I get the date, month and year only and change the format into yyyy-mm-dd in python?
thanks in advance

Use strptime from datetime library
var = "Tuesday,August22022-03:30PMWIB"
date = var.split('-')[0]
formatted_date = datetime.strptime(date, "%A,%B%d%Y")
print(formatted_date.date()) #this will get your output
Output:
2022-08-02

You can use the standard datetime library
from datetime import datetime
dates = [
"Tuesday,August22022-03:30PMWIB",
"Monday,July252022-09:33PMWIB",
"Friday,January82022-09:33PMWIB"
]
for text in dates:
text = text.split(",")[1].split("-")[0]
dt = datetime.strptime(text, '%B%d%Y')
print(dt.strftime("%Y-%m-%d"))
An alternative/shorter way would be like this (if you want the other date parts):
for text in dates:
dt = datetime.strptime(text[:-3], '%A,%B%d%Y-%I:%M%p')
print(dt.strftime("%Y-%m-%d"))
The timezone part is tricky and works only for UTC, GMT and local.
You can read more about the format codes here.
strptime() only accepts certain values for %Z:
any value in time.tzname for your machine’s locale
the hard-coded values UTC and GMT

You can convert to datetime object then get string back.
from datetime import datetime
datetime_object = datetime.strptime('Tuesday,August22022-03:30PM', '%A,%B%d%Y-%I:%M%p')
s = datetime_object.strftime("%Y-%m-%d")
print(s)

You can use the datetime library to parse the date and print it in your format. In your examples the day might not be zero padded so I added that and then parsed the date.
import datetime
date = 'Tuesday,August22022-03:30PMWIB'
date = date.split('-')[0]
if not date[-6].isnumeric():
date = date[:-5] + "0" + date[-5:]
newdate = datetime.datetime.strptime(date, '%A,%B%d%Y').strftime('%Y-%m-%d')
print(newdate)
# prints 2022-08-02

convert yyyy-mm-dd to mmm-yy in dataframe python

I am trying to convert the way month and year is presented.
I have dataframe as below
Date
2020-01-31
2020-04-30
2021-05-05
and I want to convert it in the way like month and year.
The output that I am expecting is
Date
Jan-20
Apr-20
May-21
I tried to do it with datetime but it doesn't work.
pd.to_datetime(pd.Series(df['Date'),format='%mmm-%yy')

Use .dt.strftime() to change the display format. %b-%y is the format string for Mmm-YY:
df.Date = pd.to_datetime(df.Date).dt.strftime('%b-%y')
# Date
# 0 Jan-20
# 1 Apr-20
# 2 May-21
Or if Date is the index:
df.index = pd.to_datetime(df.index).dt.strftime('%b-%y')

import pandas as pd
date_sr = pd.to_datetime(pd.Series("2020-12-08"))
change_format = date_sr.dt.strftime('%b-%Y')
print(change_format)

reference https://docs.python.org/3/library/datetime.html
%Y-%m-%d changed to ('%b-%y')
import datetime
df['Date'] = df['Date'].apply(lambda x: datetime.datetime.strptime(x,'%Y-%m-%d').strftime('%b-%y'))
# reference https://docs.python.org/3/library/datetime.html
# %Y-%m-%d changed to ('%b-%y')

Unable to subtract a day from any specific date format

I'm trying to subtract a day from this date 06-30-2019 in order to make it 06-29-2019 but can't figure out any way to achive that.
I've tried with:
import datetime
date = "06-30-2019"
date = datetime.datetime.strptime(date,'%m-%d-%Y').strftime('%m-%d-%Y')
print(date)
It surely gives me back the date I used above.
How can I subtract a day from a date in the above format?

try this
import datetime
date = "06/30/19"
date = datetime.datetime.strptime(date, "%m/%d/%y")
NewDate = date + datetime.timedelta(days=-1)
print(NewDate) # 2019-06-29 00:00:00

Your code:
date = "06-30-2019"
date = datetime.datetime.strptime(date,'%m-%d-%Y').strftime('%m-%d-%Y')
Check type of date variable.
type(date)
Out[]: str
It is in string format. To perform subtraction operation you must convert it into date format first. You can use pd.to_datetime()
# Import packages
import pandas as pd
from datetime import timedelta
# input date
date = "06-30-2019"
# Convert it into pd.to_datetime format
date = pd.to_datetime(date)
print(date)
# Substracting days
number_of_days = 1
new_date = date - timedelta(number_of_days)
print(new_date)
output:
2019-06-29 00:00:00
If you want to get rid of timestamp you can use:
str(new_date.date())
Out[]: '2019-06-29'

use timedelta
import datetime
date = datetime.datetime.strptime("06/30/19" ,"%m/%d/%y")
print( date - datetime.timedelta(days=1))

pandas.to_datetime with different length date strings

I have a column of timestamps that I would like to convert to datetime in my pandas dataframe. The format of the dates is %Y-%m-%d-%H-%M-%S which pd.to_datetime does not recognize. I have manually entered the format as below:
df['TIME'] = pd.to_datetime(df['TIME'], format = '%Y-%m-%d-%H-%M-%S')
My problem is some of the times do not have seconds so they are shorter
(format = %Y-%m-%d-%H-%M).
How can I get all of these strings to datetimes?
I was thinking I could add zero seconds (-0) to the end of my shorter dates but I don't know how to do that.

try strftime and if you want the right format and if Pandas can't recognize your custom datetime format, you should provide it explicetly
from functools import partial
df1 = pd.DataFrame({'Date': ['2018-07-02-06-05-23','2018-07-02-06-05']})
newdatetime_fmt = partial(pd.to_datetime, format='%Y-%m-%d-%H-%M-%S')
df1['Clean_Date'] = (df1.Date.str.replace('-','').apply(lambda x: pd.to_datetime(x).strftime('%Y-%m-%d-%H-%M-%S'))
.apply(newdatetime_fmt))
print(df1,df1.dtypes)
output:
Date Clean_Date
0 2018-07-02-06-05-23 2018-07-02 06:05:23
1 2018-07-02-06-05 2018-07-02 06:05:00
Date object
Clean_Date datetime64[ns]