As mentioned in comments by Henry, there is limitation of pandas timestamps because of its representation in float64, you could probably work around it by parsing the date-time using datetime library when needed, otherwise letting it stay as string or convert it to an integer
Scenario 1: If you plan on showing this value only when you print it
datetime_object = datetime.strptime('1636-09-08 00:00:00', '%Y-%m-%d %H:%M:%S')
Scenario 2: If you want to use it as a date column to retain information in the dataframe, you could additionally
datetime_object.strftime("%Y%m%d%H%M%S")
using it on a column in a pandas dataframe would yield this
df=pd.DataFrame([['1636-09-08 00:00:00'],['1635-09-09 00:00:00']], columns=['dates'])
df['str_date']=df['dates'].apply(lambda x:datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))
df.head()
|
dates |
str_date |
| 0 |
1636-09-08 00:00:00 |
1636-09-08 00:00:00 |
| 1 |
1635-09-09 00:00:00 |
1635-09-09 00:00:00 |
pandas treats this column as a object column, but when you access it, it is a datetime column
df['str_date'][0]
>>datetime.datetime(1636, 9, 8, 0, 0)
also, adding this for the sake of completeness: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-oob
