dask_expr._collection.Series.astype
dask_expr._collection.Series.astype¶
- Series.astype(dtypes)¶
Cast a pandas object to a specified dtype
dtype
.This docstring was copied from pandas.core.frame.DataFrame.astype.
Some inconsistencies with the Dask version may exist.
- Parameters
- 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 True (Not supported in Dask)
Return a copy when
copy=True
(be very careful settingcopy=False
as changes to values then may propagate to other pandas objects).Note
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
- errors{‘raise’, ‘ignore’}, default ‘raise’ (Not supported in Dask)
Control raising of exceptions on invalid data for provided dtype.
raise
: allow exceptions to be raisedignore
: suppress exceptions. On error return original object.
- Returns
- 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.
Notes
Changed in version 2.0.0: Using
astype
to convert from timezone-naive dtype to timezone-aware dtype will raise an exception. UseSeries.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[ns]