You have four main options for converting types in pandas:

1. to_numeric() – provides functionality to safely convert non-numeric types (e.g. strings) to a suitable numeric type. (See also to_datetime() and to_timedelta().)
2. astype() – convert (almost) any type to (almost) any other type (even if it’s not necessarily sensible to do so). Also allows you to convert to categorial types (very useful).
3. infer_objects() – a utility method to convert object columns holding Python objects to a pandas type if possible.
4. convert_dtypes() – convert DataFrame columns to the “best possible” dtype that supports pd.NA (pandas’ object to indicate a missing value).

dataframe = dataframe.astype({'col_name_1':'int','col_name_2':'float64', etc. ...})