DataFrame.astype() ⚬ Entries

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DataFrame.astype() ⚬｜Documentation｜1st｜20251021001246-00-⌔

pandas.DataFrame.astype — pandas 2.3.3 documentation#pandas.DataFrame.astype

DataFrame.astype(dtype, copy=<no_default>, errors='raise')

Cast a pandas object to a specified dtype dtype.

This method allows the conversion of the data types of pandas objects, including DataFrames and Series, to the specified dtype. It supports casting entire objects to a single data type or applying different data types to individual columns using a mapping.

Parameters:

dtype: str, data type, Series or Mapping of column name -> data type

Use a str, numpy.dtype, pandas.ExtensionDtype or Python type to cast entire pandas object to the same type. Alternatively, use a mapping, e.g. {col: dtype, …}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame’s columns to column-specific types.

copy: bool, default False

This keyword is now ignored; changing its value will have no impact on the method.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.

errors: {‘raise’, ‘ignore’}, default ‘raise’

Control raising of exceptions on invalid data for provided dtype.

• raise: allow exceptions to be raised
• ignore: suppress exceptions. On error return original object.

Returns:

same type as caller

The pandas object casted to the specified dtype.

See also:

to_datetime

Convert argument to datetime.

to_timedelta

Convert argument to timedelta.

to_numeric

Convert argument to a numeric type.

numpy.ndarray.astype

Cast a numpy array to a specified type.

Notes:

Changed in version 2.0.0: Using astype to convert from timezone-naive dtype to timezone-aware dtype will raise an exception. Use Series.dt.tz_localize() instead.

Examples:

Create a DataFrame:

>>> d = {"col1": [1, 2], "col2": [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df.dtypes
col1    int64
col2    int64
dtype: object

Cast all columns to int32:

>>> df.astype("int32").dtypes
col1    int32
col2    int32
dtype: object

Cast col1 to int32 using a dictionary:

>>> df.astype({"col1": "int32"}).dtypes
col1    int32
col2    int64
dtype: object

Create a series:

>>> ser = pd.Series([1, 2], dtype="int32")
>>> ser
0    1
1    2
dtype: int32
>>> ser.astype("int64")
0    1
1    2
dtype: int64

Convert to categorical type:

>>> ser.astype("category")
0    1
1    2
dtype: category
Categories (2, int32): [1, 2]

Convert to ordered categorical type with custom ordering:

>>> from pandas.api.types import CategoricalDtype
>>> cat_dtype = CategoricalDtype(categories=[2, 1], ordered=True)
>>> ser.astype(cat_dtype)
0    1
1    2
dtype: category
Categories (2, int64): [2 < 1]

Create a series of dates:

>>> ser_date = pd.Series(pd.date_range("20200101", periods=3))
>>> ser_date
0   2020-01-01
1   2020-01-02
2   2020-01-03
dtype: datetime64[us]

Printed 2026-06-28.

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