Pandas - converting out of order string date time - python-3.x

Question

I have a DataFrame column that has string values for date/time (Input data). I need to convert it into a semi-timestamp format (Desired output data). There are rows that are blank and need to remain blank. I use quotes for illustrative purposes. I am using strptime but getting an error (see below).
Input data (String):
Mar 8 12:00 PM 2020
' '
Mar 8 1:00 PM 2020
Mar 8 6:00 PM 2020
Mar 9 8:00 AM 2020
Desired output data:
3/8/2020 12:00:00
' '
3/8/2020 13:00:00
3/8/2020 18:00:00
3/9/2020 08:00:00
Code:
import datetime as dt
df['date'].apply(lambda x: dt.datetime.strptime(x, '%b %d %H:%M %p %Y'))
Error:
ValueError: time data '' does not match format '%b %d %H:%M %p %Y'
How can I rewrite this code to get the desired output?

Answer 1

For me working to_datetime with format similar like yoour with %I for select hours in 12H format, also is added errors='coerce' for missing values (NaT) if some value not matching:
df['date'] = pd.to_datetime(df['date'], format='%b %d %I:%M %p %Y', errors='coerce')
print (df)
date
0 2020-03-08 12:00:00
1 NaT
2 2020-03-08 13:00:00
3 2020-03-08 18:00:00
4 2020-03-09 08:00:00
Last for custom format use Series.dt.strftime with Series.replace:
df['date'] = (pd.to_datetime(df['date'], format='%b %d %I:%M %p %Y', errors='coerce')
.dt.strftime('%m/%d/%y %H:%M:%S')
.replace('NaT', ''))
print (df)
date
0 03/08/20 12:00:00
1
2 03/08/20 13:00:00
3 03/08/20 18:00:00
4 03/09/20 08:00:00
Or replace multiple spoaces to one space:
df['date'] = (pd.to_datetime(df['date'].replace('\s+', ' ', regex=True), format='%b %d %I:%M %p %Y', errors='coerce')
.dt.strftime('%m/%d/%y %H:%M:%S')
.replace('NaT', ''))
print (df)
date
0 03/08/20 12:00:00
1
2 03/08/20 13:00:00
3 03/08/20 18:00:00
4 03/09/20 08:00:00