Astype vs pd.to_datetime

Astype and pandas date time

Ever wondered that we could be using date time conversion in python could lead us to two different methods that perform same job but little do we know that their real working principle.

As we can see below that astype and pd.to_datetime are used for converting a column of dtype say from string or object to Datetime format. By doing so we can separate them for days, week, no of days or day of week by using .dt.dayofweek as an example. 

astype

• Purpose: General type conversion.
• Usage: Converts a pandas object (like a DataFrame column) to a specified dtype.
• Example: If you have a column of strings representing dates and you want to convert them to datetime objects, you can use astype.

df['date_column'] = df['date_column'].astype('datetime64[ns]')

pd.to_datetime

• Purpose: Specialized function for parsing date and time strings to datetime objects.
• Usage: Converts argument to datetime, optionally with more control over parsing, errors handling, and inferencing.
• Example: If you have a column of strings and you want to convert them to datetime objects, you can use pd.to_datetime.

df['date_column'] = pd.to_datetime(df['date_column']) 

# Get day of week with 0=Monday, 6=Sunday  

day_of_week = df['date_col'].dt.dayofweek 

Key Differences

1. Functionality:

   • astype is a general-purpose method for casting types and works with any dtype.
   • pd.to_datetime is specifically designed to handle date and time conversion, offering more options and better handling of edge cases.

2. Flexibility:

   • pd.to_datetime can infer formats, handle errors, and deal with time zones more robustly compared to astype.

3. Performance:

   • astype may be faster for straightforward conversions but lacks the advanced parsing capabilities of pd.to_datetime.

In most cases, for date and time conversion, pd.to_datetime is recommended due to its flexibility and robustness. Use astype when you are certain of the format and need a quick conversion without additional parsing.