dask.dataframe.Series.astype

Series.astype(dtype)

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
dtypedata 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.

copybool, default True (Not supported in Dask)

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’ (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
castedsame 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

Deprecated since version 1.3.0: Using astype to convert from timezone-naive dtype to timezone-aware dtype is deprecated and will raise in a future version. 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, int64): [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]

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

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]