Series.astype(dtype, copy=<no_default>, errors='raise')[source]#

Cast a pandas object to a specified dtype dtype.

dtypestr, 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.

copybool, default False

Return a copy when copy=True (be very careful setting copy=False as changes to values then may propagate to other pandas objects).


The copy keyword will change behavior in pandas 3.0. Copy-on-Write will be enabled by default, which means that all methods with a copy keyword will use a lazy copy mechanism to defer the copy and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.options.mode.copy_on_write = True

Deprecated since version 3.0.0.

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.

same type as caller

The pandas object casted to the specified dtype.

See also


Convert argument to datetime.


Convert argument to timedelta.


Convert argument to a numeric type.


Cast a numpy array to a specified type.


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.


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[ns]