mars.dataframe.Series.astype#

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

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 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.

Returns

casted

Return type

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

Create a DataFrame:

>>> import mars.dataframe as md
>>> df = md.DataFrame(pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}))
>>> 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 = md.Series(pd.Series([1, 2], dtype='int32'))
>>> ser.execute()
0    1
1    2
dtype: int32
>>> ser.astype('int64').execute()
0    1
1    2
dtype: int64

Convert to categorical type:

>>> ser.astype('category').execute()
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).execute()
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 = md.Series(pd.Series([1, 2]))
>>> s2 = s1.astype('int64', copy=False)
>>> s1.execute()  # note that s1[0] has changed too
0     1
1     2
dtype: int64