pandas.Series.astype — pandas 2.2.3 documentation (original) (raw)

Series.astype(dtype, copy=None, errors='raise')[source]#

Cast a pandas object to a specified dtype dtype.

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

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-Writewill be enabled by default, which means that all methods with acopy 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’

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

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[ns]