dask.dataframe.Series.astype

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dask.dataframe.Series.astype#

Series.astype(dtypes)#

Cast a pandas object to a specified dtype dtype.

This docstring was copied from pandas.DataFrame.astype.

Some inconsistencies with the Dask version may exist.

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:
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 (Not supported in Dask)

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’ (Not supported in Dask)

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]
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