NumPy astype()

astype() Syntax

The syntax of astype() is:

ndarray.astype(dtype, order = 'K', casting = 'unsafe', subok = True, copy = True)

astype() Arguments

The astype() method takes five arguments:

• dtype - desired data type for the new array
• order (optional) - memory layout order of the returned array
• casting (optional) - casting behavior when converting data types
• subok (optional) - determines whether to subclass the output array if the data type is changed or to return a base-class array
• copy (optional) - creates a copy if True, modifies the original array if False

astype() Return Value

The astype() method returns the modified array:

• If the copy argument is True, a new array is returned.
• If the copy argument is False, the original array is modified.

Example 1: Convert an Integer Array to Different Data Types

Output

Original Array: [0 1 2 3 4 5]
Float Array: [0. 1. 2. 3. 4. 5.]
Complex Array: [0.+0.j 1.+0.j 2.+0.j 3.+0.j 4.+0.j 5.+0.j]
Boolean Array: [False  True  True  True  True  True]
String Array: ['0' '1' '2' '3' '4' '5']

Using Optional order Argument in astype()

The order argument specifies the order in which the array elements are stored in memory.

The order can be:

• 'C' - flattens the elements row-wise (in C-style order)
• 'F' - flattens the elements column-wise (in Fortran-style order)
• 'A' - tries to preserve the original array's order, otherwise defaults to C-order.
• 'K' - flattens the elements in the order they occur in memory, and uses C-order by default.

Let's see an example.

Output

C order Array:
[[1. 2. 3.]
 [4. 5. 6.]]
C_CONTIGUOUS : True
F_CONTIGUOUS : False 

F order Array:
 [[1. 2. 3.]
 [4. 5. 6.]]
C_CONTIGUOUS : False
F_CONTIGUOUS : True 

A order Array:
 [[1. 2. 3.]
 [4. 5. 6.]]
C_CONTIGUOUS : True
F_CONTIGUOUS : False 

K order Array:
 [[1. 2. 3.]
 [4. 5. 6.]]
C_CONTIGUOUS : True
F_CONTIGUOUS : False 

Using Optional casting Argument in astype()

The casting argument specifies the casting behavior when converting data types.

The casting can be:

• 'no' - data types should not be cast at all
• 'equiv' - only byte-order changes are allowed
• 'safe' - only casts which can preserve values are allowed
• 'same_kind' - only safe casts or casts within a kind are allowed
• 'unsafe' - any data conversions may be done

Let's see an example.

Output

Array with 'no' casting: [1 2 3 4 5]
Array with 'equiv' casting: [1 2 3 4 5]
Array with 'safe' casting: [1. 2. 3. 4. 5.]
Array with 'same_kind' casting: [1 2 3 4 5]
Array with 'unsafe' casting: ['1' '2' '3' '4' '5']

Using Optional subok Argument in astype()

The subok argument specifies whether to use subclass instances, if available, for the returned array.

The subok can be:

• True- resulting array maintains the subclass
• False - resulting array doesn't maintain the subclass

Output

Original Array Type: <class '__main__.CustomArray'>
Float Array1 Type: <class '__main__.CustomArray'>
Float Array2 Type: <class 'numpy.ndarray'>