pandas.DataFrame.astype

DataFrame.astype(dtype, copy=True, errors='raise')[source]

Cast a pandas object to a specified dtype dtype.

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

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
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.

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]

Datetimes are localized to UTC first before converting to the specified timezone:

>>> ser_date.astype('datetime64[ns, US/Eastern]')
0   2019-12-31 19:00:00-05:00
1   2020-01-01 19:00:00-05:00
2   2020-01-02 19:00:00-05:00
dtype: datetime64[ns, US/Eastern]