Final answer:
To convert an object to a datetime in Pandas, use the pd.to_datetime() function, which can handle various formats. For non-standard formats, specify the format parameter with the correct date format. After conversion, time series analyses can be performed with the datetime objects.
Step-by-step explanation:
To convert an object to a datetime in Pandas, you can use the pd.to_datetime() function. This function is capable of converting a wide range of formats into a Pandas datetime object. If your data is in a non-standard format, you may have to use the format parameter to specify the exact format of your dates.
Here is an example of how to convert a series of date strings into datetime objects:
import pandas as pd
dates = pd.Series(['2023-01-01', '2023-01-02', '2023-01-03'])
datetime_series = pd.to_datetime(dates)
If your dates are in a format such as 'Day-Month-Year', you need to specify that format:
import pandas as pd
dates = pd.Series(['01-02-2023', '02-03-2023', '03-04-2023'])
datetime_series = pd.to_datetime(dates, format='%d-%m-%Y')
Once you have converted your series to datetime, you can perform various time series analyses in Pandas.