astype(self: 'NDFrameT', dtype, copy: 'bool_t' = True, errors: 'str' = 'raise') -> 'NDFrameT'
Using astype
to convert from timezone-naive dtype to timezone-aware dtype is deprecated and will raise in a future version. Use :None:meth:`Series.dt.tz_localize` instead.
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.
Return a copy when copy=True
(be very careful setting copy=False
as changes to values then may propagate to other pandas objects).
Control raising of exceptions on invalid data for provided dtype.
Cast a pandas object to a specified dtype dtype
.
numpy.ndarray.astype
Cast a numpy array to a specified type.
to_datetime
Convert argument to datetime.
to_numeric
Convert argument to a numeric type.
to_timedelta
Convert argument to timedelta.
Create a DataFrame:
This example is valid syntax, but we were not able to check execution>>> 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:
This example is valid syntax, but we were not able to check execution>>> df.astype('int32').dtypes col1 int32 col2 int32 dtype: object
Cast col1 to int32 using a dictionary:
This example is valid syntax, but we were not able to check execution>>> df.astype({'col1': 'int32'}).dtypes col1 int32 col2 int64 dtype: object
Create a series:
This example is valid syntax, but we were not able to check execution>>> ser = pd.Series([1, 2], dtype='int32')This example is valid syntax, but we were not able to check execution
... ser 0 1 1 2 dtype: int32
>>> ser.astype('int64') 0 1 1 2 dtype: int64
Convert to categorical type:
This example is valid syntax, but we were not able to check execution>>> ser.astype('category') 0 1 1 2 dtype: category Categories (2, int64): [1, 2]
Convert to ordered categorical type with custom ordering:
This example is valid syntax, but we were not able to check execution>>> 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:
This example is valid syntax, but we were not able to check execution>>> ser_date = pd.Series(pd.date_range('20200101', periods=3))See :
... ser_date 0 2020-01-01 1 2020-01-02 2 2020-01-03 dtype: datetime64[ns]
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