pandas.Series.astype#
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. , 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 , 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.
Convert argument to datetime.
Convert argument to timedelta.
Convert argument to a numeric type.
Cast a numpy array to a specified type.
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.
>>> 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
>>> 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]
>>> 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]