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How to convert Pandas Series of strings to Pandas datetime with non-standard formats that contain dates before 1970

I have a column of dates in the following format:
Jan-85
Apr-99
Nov-01
Feb-65
Apr-57
Dec-19
I want to convert this to a pandas datetime object.
The following syntax works to convert them:
pd.to_datetime(temp, format='%b-%y')
where temp is the pd.Series object of dates. The glaring issue here of course is that dates that are prior to 1970 are being wrongly converted to 20xx.
I tried updating the function call with the following parameter:
pd.to_datetime(temp, format='%b-%y', origin='1950-01-01')
However, I am getting the error:
Name: temp, Length: 42537, dtype: object' is not compatible with origin='1950-01-01'; it must be numeric with a unit specified
I tried specifying a unit as it said, but I got a different error citing that the unit cannot be specified alongside a format.
Any ideas how to fix this?

Just #DudeWah's logic, but improving upon the code:
def days_of_future_past(date,chk_y=pd.Timestamp.today().year):
return date.replace(year=date.year-100) if date.year > chk_y else date
temp = pd.to_datetime(temp,format='%b-%y').map(days_of_future_past)
Output:
>>> temp
0 1985-01-01
1 1999-04-01
2 2001-11-01
3 1965-02-01
4 1957-04-01
5 2019-12-01
6 1965-05-01
Name: date, dtype: datetime64[ns]

Gonna go ahead and answer my own question so others can use this solution if they come across this same issue. Not the greatest, but it gets the job done. It should work until 2069, so hopefully pandas will have a better solution to this by then lol
Perhaps someone else will post a better solution.
def wrong_date_preprocess(data):
"""Correct date issues with pre-1970 dates with whacky mon-yy format."""
df1 = data.copy()
dates = df1['date_column_of_interest']
# use particular datetime format with data; ex: jan-91
dates = pd.to_datetime(dates, format='%b-%y')
# look at wrongly defined python dates (pre 1970) and get indices
date_dummy = dates[dates > pd.Timestamp.today().floor('D')]
idx = list(date_dummy.index)
# fix wrong dates by offsetting 100 years back dates that defaulted to > 2069
dummy2 = date_dummy.apply(lambda x: x.replace(year=x.year - 100)).to_list()
dates.loc[idx] = dummy2
df1['date_column_of_interest'] = dates
return(df1)

Data parsing in pandas, python

I have an excel file with many columns, one of them, 'Column3' is date with some text in it, basically it looks like that:
26/05/20
XXX
YYY
12/05/2020
The data is written in DD/MM/YY format but pandas, just like excel, thinks that 12/05/2020 it's 05 Dec 2020 while it is 12 May 2020. (My windows is set to american date format)
Important note: when I open stock excel file, cells with 12/05/2020 already are Date type, trying to convert it to text it gives me 44170 which will give me wrong date if I just reformat it into DD/MM/YY
I added this line of code:
iport pandas as pd
dateparse = lambda x: pd.datetime.strptime(x,'%d/%m/%y')
df = pd.read_excel("my_file.xlsx", parse_dates=['Column3'], date_parser=dateparse)
But the text in the column generates an error.
ValueError: time data 'XXX' does not match format '%d/%m/%y'
I went a step further and manually removed all text (obviously I can't do it all the time) to see whether it works or nor, but then I got following error
dateparse = lambda x: pd.datetime.strptime(x,'%d/%m/%y')
TypeError: strptime() argument 1 must be str, not datetime.datetime
I also tried this:
df['Column3'] = pd.to_datetime(df.Column3, format ='%d/%m/%y', errors="coerce")
# if I make errors="ignore" it doesn't change anything.
in that case my 26/05/20 was correctly converted to 26 May 2020 but I lost all my text data(it's ok) and other dates which didn't match with my format argument. Because previously they were recognized as American type date.
My objective is to convert the data in Column3 to the same format so I could apply filters with pandas.
I think it's couple solutions:
tell Pandas to not convert text to date at all (but it is already saved as Date type in stock file, will it work?)
somehow ignore text values and use date_parser= method co convert add dates to DD/MM/YY
with help of pd.to_datetime convert 26/05/20 to 26 May 2020 and than convert 2020-09-06 00:00:00 to 9 June 2020 (seems to be the simplest one but ignore argument doesn't work.)
Here's link to small sample file https://easyupload.io/ca5p6w

You can pass a date_parser to read_excel:
dateparser = lambda x: pd.to_datetime(x, dayfirst=True)
pd.read_excel('test.xlsx', date_parser = dateparser)

Posting this as an answer, since it's too long for a comment
The problem originates in Excel. If I open it in Excel, I see 2 strings that look like dates 26/05/20, 05/12/2020 and 06/02/2020. Note the difference between the 20 and 2020 On lines 24 and 48 I see dates in Column4. This seems to indicate the Excel is put together. Is this Excel assembled by copy-paste, or programmatically?
loading it with just pd.read_excel gives these results for the dates:
26/05/20
2020-12-05 00:00:00
2020-02-06 00:00:00
If I do df["Column3"].apply(type)
gives me
str
<class 'datetime.datetime'>
<class 'datetime.datetime'>
So in the Excel file these are marked as datetime.
Loading them with df = pd.read_excel(DATA_DIR / "sample.xlsx", dtype={"Column3": str}) changes the type of all to str, but does not change the output.
If you open the extract the file, and go look at the xml file xl\worksheets\sheet1.xml directly and look for cell C26, you see it as 44170, while C5 is 6, which is a reference to 26/05/20 in xl/sharedStrings.xml
How do you 'make' this Excel file? This can best be solved in how this file is put together.
Workaround
As a workaround, you can convert the dates piecemeal. The different format allows this:
format1 = "%d/%m/%y"
format2 = "%Y-%d-%m %H:%M:%S"
Then you can do pd.to_datetime(dates, format=format1, errors="coerce") to only get the first dates, and NaT for the ones not according to the format. Then you use combine_first to fill the voids.
dates = df["Column3"] # of the one imported with dtype={"Column3": str}
dates_parsed = (
pd.to_datetime(dates, format=format1, errors="coerce")
.combine_first(pd.to_datetime(dates, format=format2, errors="coerce"))
.astype(object)
.combine_first(dates)
)
The astype(object) is needed to fill in the empty places with the string values.

I think, first you should import the file without date parsing then convert it to date format using following:
df['column3']= pd.to_datetime(df['column3'], errors='coerce')
Hope this will work

How do I change the Date but not the Time of a Timestamp within a dataframe column?

Python 3.6.0
I am importing a file with Unix timestamps.
I’m converting them to Pandas datetime and rounding to 10 minutes (12:00, 12:10, 12:20,…)
The data is collected from within a specified time period, but from different dates.
For our analysis, we want to change all dates to the same dates before doing a resampling.
At present we have a reduce_to_date that is the target for all dates.
current_date = pd.to_datetime('2017-04-05') #This will later be dynamic
reduce_to_date = current_date - pd.DateOffset(days=7)
I’ve tried to find an easy way to change the date in a series without changing the time.
I was trying to avoid lengthy conversions with .strftime().
One method that I've almost settled is to add the reduce_to_date and df['Timestamp'] difference to df['Timestamp']. However, I was trying to use the .date() function and that only works on a single element, not on the series.
GOOD!
passed_df['Timestamp'][0] = passed_df['Timestamp'][0] + (reduce_to_date.date() - passed_df['Timestamp'][0].date())
NOT GOOD
passed_df['Timestamp'][:] = passed_df['Timestamp'][:] + (reduce_to_date.date() - passed_df['Timestamp'][:].date())
AttributeError: 'Series' object has no attribute 'date'
I can use a loop:
x=1
for line in passed_df['Timestamp']:
passed_df['Timestamp'][x] = line + (reduce_to_date.date() - line.date())
x+=1
But this throws a warning:
C:\Users\elx65i5\Documents\Lightweight Logging\newmain.py:60: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
The goal is to have all dates the same, but leave the original time.
If we can simply specify the replacement date, that’s great.
If we can use mathematics and change each date according to a time delta, equally as great.
Can we accomplish this in a vectorized fashion without using .strftime() or a lengthy procedure?

If I understand correctly, you can simply subtract an offset
passed_df['Timestamp'] -= pd.offsets.Day(7)
demo
passed_df=pd.DataFrame(dict(
Timestamp=pd.to_datetime(['2017-04-05 15:21:03', '2017-04-05 19:10:52'])
))
# Make sure your `Timestamp` column is datetime.
# Mine is because I constructed it that way.
# Use
# passed_df['Timestamp'] = pd.to_datetime(passed_df['Timestamp'])
passed_df['Timestamp'] -= pd.offsets.Day(7)
print(passed_df)
Timestamp
0 2017-03-29 15:21:03
1 2017-03-29 19:10:52
using strftime
Though this is not ideal, I wanted to make a point that you absolutely can use strftime. When your column is datetime, you can use strftime via the dt date accessor with dt.strftime. You can create a dynamic column where you specify the target date like this:
pd.to_datetime(passed_df.Timestamp.dt.strftime('{} %H:%M:%S'.format('2017-03-29')))
0 2017-03-29 15:21:03
1 2017-03-29 19:10:52
Name: Timestamp, dtype: datetime64[ns]

I think you need convert df['Timestamp'].dt.date to_datetime, because output of date is python date object, not pandas datetime object:
df=pd.DataFrame({'Timestamp':pd.to_datetime(['2017-04-05 15:21:03','2017-04-05 19:10:52'])})
print (df)
Timestamp
0 2017-04-05 15:21:03
1 2017-04-05 19:10:52
current_date = pd.to_datetime('2017-04-05')
reduce_to_date = current_date - pd.DateOffset(days=7)
df['Timestamp'] = df['Timestamp'] - reduce_to_date + pd.to_datetime(df['Timestamp'].dt.date)
print (df)
Timestamp
0 2017-04-12 15:21:03
1 2017-04-12 19:10:52

How to convert unusual 24 hour date time format in python?

I have a dataframe column full datetime type that are in the format
2016Oct03:14:38:33
Right now, the data type of this column of the dataframe is String. I would like to convert it into datetime in order to be able perform some numerical operations like subtractions on them. I have tried specifying the format while using pd.to_datetime but as the time is in a 24 hr format, it is throwing up an error. What is the best way to do this? Thanks in advance!

There doesn't seem to be anything unusual about the time format at all; 24 hour is absolutely standard.
Just the normal strptime is fine:
datetime.strptime(my_date, '%Y%b%d:%H:%M:%S')

You need to_datetime with parameter format:
df = pd.DataFrame({'dates':['2016Oct03:14:38:33',
'2016Oct03:14:38:33',
'2016Oct03:14:38:33']})
print (df)
dates
0 2016Oct03:14:38:33
1 2016Oct03:14:38:33
2 2016Oct03:14:38:33
df.dates = pd.to_datetime(df.dates, format='%Y%b%d:%H:%M:%S')
print (df)
dates
0 2016-10-03 14:38:33
1 2016-10-03 14:38:33
2 2016-10-03 14:38:33

Duplicated question
Use datetime.strptime
Ex:
from datetime import datetime
date_object = datetime.strptime('2016Oct03:14:38:33', '%Y%b%d:%H:%M:%S')
Doc : https://docs.python.org/2/library/datetime.html

pd.to_datetime change date format producing wrong dates

I have extracted the table below from a csv file :
timestamp user_id cost val1
01/01/2011 1 1 3
01/07/2012 1 19 57
01/09/2013 1 21 63
01/02/2011 2 20 8
And I for this purpose, I used the following statements :
import pandas as pd
newnames = ['date','user_id', 'cost', 'val1']
df = pd.read_csv('expenses.csv', names = newnames, header = False)
df['timestamp'] = pd.to_datetime(df['timestamp'],format='%d%m%Y')
But the dates of table obtained in df are wrong : months and days are permutated without reason, i.e 01/09/2013 becomes 09/01/2013 without reason.
Many thanks in advance for your help.

Hi it happens sometimes due to US/Europe time parsing compatibilties.
What I follow is,
Case 1
If you are sure that your input file is having the time stamp in a correct format, and you want to use it on top of default parser, like in this case, use (as advised by #filmor) dayfirst option in pd.to_datetime
pd.to_datetime(df['timestamp'], dayfirst=True)
Case 2
When you are not sure about the correctness of the format, use the default,
pd.to_datetime(df['timestamp'], dayfirst=True)

It is most certainly not random. Pandas defaults to the US date format and falls back if that doesn't make sense, i.e. "12/3/2014" becomes 2014-12-03 while "13/3/2014" will be parsed as 2014-03-13.
You can pass dayfirst=True to pd.read_csv to force European-style date parsing.