pandas.Series.astype¶
-
Series.
astype
(dtype, copy=True, errors='raise', **kwargs)[source]¶ Cast a pandas object to a specified dtype
dtype
.Parameters: - dtype : data type, or dict of column name -> data type
Use a numpy.dtype or Python type to cast entire pandas object to the same type. Alternatively, use {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 settingcopy=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 raisedignore
: suppress exceptions. On error return original object
New in version 0.20.0.
- kwargs : keyword arguments to pass on to the constructor
Returns: - casted : same type as caller
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.
Examples
>>> 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, int64): [1, 2]
Convert to ordered categorical type with custom ordering:
>>> cat_dtype = pd.api.types.CategoricalDtype( ... categories=[2, 1], ordered=True) >>> ser.astype(cat_dtype) 0 1 1 2 dtype: category Categories (2, int64): [2 < 1]
Note that using
copy=False
and changing data on a new pandas object may propagate changes:>>> s1 = pd.Series([1,2]) >>> s2 = s1.astype('int64', copy=False) >>> s2[0] = 10 >>> s1 # note that s1[0] has changed too 0 10 1 2 dtype: int64