Series.astype(dtype, copy=True, errors='raise')#

Cast a pandas object to a specified dtype dtype.

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

  • 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.

Return type

same type as caller

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(pandas): 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]

This docstring was copied from pandas.core.series.Series.