# pandas.Panel.astype

`Panel.``astype`(*args, **kwargs)

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

  Parameters:

  dtype : data 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.

  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 : {‘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

  > New in version 0.20.0.

  raise_on_error : raise on invalid input

  > Deprecated since version 0.20.0: Use `errors` instead

  kwargs : keyword arguments to pass on to the constructor

  Returns: | casted : type of caller

  See also

  `pandas.to_datetime`
    Convert argument to datetime.
  `pandas.to_timedelta`
    Convert argument to timedelta.
  `pandas.to_numeric`
    Convert argument to a numeric type.
  `numpy.ndarray.astype`
    Cast a numpy array to a specified type.

  Examples

      >>> 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:

      >>> ser.astype('category', ordered=True, categories=[2, 1])
      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('int', copy=False)
      >>> s2[0] = 10
      >>> s1  # note that s1[0] has changed too
      0    10
      1     2
      dtype: int64

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