DataFrameastype

DataFrame.astype is a pandas method used to convert the data type of one or more columns in a DataFrame. It can accept a single dtype to apply to all columns or a dictionary mapping column names to dtypes, enabling per-column casting. For example, a DataFrame df can be cast with df.astype({'A': 'float64', 'B': 'Int64'}) to convert column A to float64 and column B to the nullable integer type Int64.

The method returns a new DataFrame by default, with copy semantics controlled by the copy parameter. If copy is True (the default), a new object is created; if copy is False, a view may be returned when no changes are required.

Error handling is controlled by the errors parameter, which accepts 'raise' (the default) to raise if a column cannot be cast, or 'ignore' to leave non-convertible columns unchanged.

Astype supports numpy dtypes like 'int64', 'float64', 'bool', and object types, as well as pandas extension dtypes such as 'category', 'string', and 'Int64' (nullable integers) or 'Boolean' (nullable booleans). When using standard numpy integer types, missing values (NaN) may cause casting to fail; using nullable dtypes like 'Int64' can preserve missing values.

Note that for converting strings to datetimes, casting with astype may not reliably parse arbitrary date formats; use pandas.to_datetime for parsing when dealing with date strings. In summary, astype provides flexible, column-aware type casting for DataFrames with straightforward, vectorized operations.