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Weird Date formats part of column in YY-mm-dd and the rest of columns YY-dd-mm

This is a strange one but I have an original excel with 10/11/2018 and the above problem happens when i convert column to datetime using:
df.Date = pd.to_datetime(df['Date'])
So the date column is 2018-01-11, then the date/months are equal for example 2018-11-11, it swaps the format of previous row and the row is now
''2018-11-12''
''2018-11-13''
ive tried to write a for loop for each entry changing the series but get error cant change series, then i tried writing a loop but get the time error
for date_ in jda.Date:
jda.Date[date_] = jda.Date[date_].strftime('%Y-%m-%d')
KeyError: Timestamp('2019-05-17 00:00:00')
Beow is a pic of where the forat changes
Thank you for your help

Solution if dates are saved like strings:
I think problem is wrong parsed datetimes, because by default are 10/11/2018 parsed to 11.October 2018, so if need parse to 10. November 2018 format add dayfirst=True parameter in to_datetime:
df.Date = pd.to_datetime(df['Date'], dayfirst=True)
Or you can specify format e.g. %d/%m/%Y for DD/MM/YYYY:
df.Date = pd.to_datetime(df['Date'], format='%d/%m/%Y')

csv Pandas datetime convert time to seconds

I work with data from Datalogger and the timestap is not supported by datetime in the Pandas Dataframe.
I would like to convert this timestamp into a format pandas knows and the then convert the datetime into seconds, starting with 0.
>>>df.time
0 05/20/2019 19:20:27:374
1 05/20/2019 19:20:28:674
2 05/20/2019 19:20:29:874
3 05/20/2019 19:20:30:274
Name: time, dtype: object
I tried to convert it from the object into datetime64[ns]. with %m or %b for month.
df_time = pd.to_datetime(df["time"], format = '%m/%d/%y %H:%M:%S:%MS')
df_time = pd.to_datetime(df["time"], format = '%b/%d/%y %H:%M:%S:%MS')
with error: redefinition of group name 'M' as group 7; was group 5 at position 155
I tried to reduce the data set and remove the milliseconds without success.
df['time'] = pd.to_datetime(df['time'],).str[:-3]
ValueError: ('Unknown string format:', '05/20/2019 19:20:26:383')
or is it possible to just subtract the first time line from all the other values in the column time?

Use '%m/%d/%Y %H:%M:%S:%f' as format instead of '%m/%d/%y %H:%M:%S:%MS'
Here is the format documentation for future reference

I am not exactly sure what you are looking for but you can use the above example to format your output and then you can remove items from your results like the microseconds this way:
date = str(datetime.now())
print(date)
2019-07-28 14:04:28.986601
print(date[11:-7])
14:04:28
time = date[11:-7]
print(time)
14:04:28

Convert date string YYYY-MM-DD to YYYYMM in pandas

Is there a way in pandas to convert my column date which has the following format '1997-01-31' to '199701', without including any information about the day?
I tried solution of the following form:
df['DATE'] = df['DATE'].apply(lambda x: datetime.strptime(x, '%Y%m'))
but I obtain this error : 'ValueError: time data '1997-01-31' does not match format '%Y%m''
Probably the reason is that I am not including the day in the format. Is there a way better to pass from YYYY-MM_DD format to YYYYMM in pandas?

One way is to convert the date to date time and then use strftime. Just a note that you do lose the datetime functionality of the date
df = pd.DataFrame({'date':['1997-01-31' ]})
df['date'] = pd.to_datetime(df['date'])
df['date'] = df['date'].dt.strftime('%Y%m')
date
0 199701

Might not need to go through the datetime conversion if the data are sufficiently clean (no incorrect strings like 'foo' or '001231'):
df = pd.DataFrame({'date':['1997-01-31', '1997-03-31', '1997-12-18']})
df['date'] = [''.join(x.split('-')[0:2]) for x in df.date]
# date
#0 199701
#1 199703
#2 199712
Or if you have null values:
df['date'] = df.date.str.replace('-', '').str[0:6]

Pandas to_datetime not formatting as expected

I have a data frame with a column 'Date' with data type datetime64. The values are in YYYY-MM-DD format.
How can I convert it to YYYY-MM format and use it as a datetime64 object itself.
I tried converting my datetime object to a string in YYYY-MM format and then back to datetime object in YYYY-MM format but it didn't work.
Original data = 1988-01-01.
Converting datatime object to string in YY-MM format
df['Date']=df['Date'].dt.strftime('%Y-%m')
This worked as expected, my column value became
1988-01
Converting the string back to datetime object in Y-m format
df['Date']=pd.to_datetime(df['Date'],format= '%Y-%m')
I was expecting the Date column in YYYY-MM format but it became YYYY-MM-DD format.
1988-01-01
Can you please let me know if I am missing something.
Thanks

It is expected behaviour, in datetimes the year, month and day arguments are required.
If want remove days need month period by to_period:
df['Date'] = df['Date'].dt.to_period('M')
df['Date'] = pd.to_datetime(df['Date'],format= '%Y-%m').dt.to_period('M')
Sample:
df = pd.DataFrame({'Date':pd.to_datetime(['1988-01-01','1999-01-15'])})
print (df)
Date
0 1988-01-01
1 1999-01-15
df['Date'] = df['Date'].dt.to_period('M')
print (df)
Date
0 1988-01
1 1999-01

Parse timestamp having hour beyond 23 in python

I am learning python and came across an issue where I am trying to read timestamp from CSV file in below format,
43:32.0
here 43 is at hours position and convert it to DateTime format in Pandas.
I tried code,
df['time'] = df['time'].astype(str).str[:-2]
df['time'] = pd.to_datetime(df['time'], errors='coerce')
But, this is converting all values to NaT
I need the output to be in format - mm/dd/yyyy hh:mm:ss

I'm going to assume that this is a Date for 11-29-17 (today's date)?
I believe you need to add an extra 0: in the beginning of the string. Basic Example:
import pandas as pd
# creating a dataframe of your string
df1 = pd.DataFrame({'A':['43:32.0']})
# adding '0:' to the front
df1['A'] = '0:' + df1['A'].astype(str)
# making new column to show the output
df1['B'] = pd.to_datetime(df1['A'], errors='coerce')
#output
A B
0 0:43:32.0 2017-11-29 00:43:32