Skip to main content
`Ctrl`+`K`

# pandas.Series.astype

Series.astype(dtype, copy=<no_default>, errors='raise')

  Cast a pandas object to a specified dtype `dtype`.

  This method allows the conversion of the data types of pandas objects, including DataFrames and Series, to the specified dtype. It supports casting entire objects to a single data type or applying different data types to individual columns using a mapping.

  Parameters:
    dtype str, 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.

    copy bool, default False

      This keyword is now ignored; changing its value will have no impact on the method.

      Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.

    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

      The pandas object casted to the specified `dtype`.

  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.

  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[us]