# numpy.asarray¶

numpy.asarray(a, dtype=None, order=None)[source]

Convert the input to an array.

Parameters : a : array_like Input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays. dtype : data-type, optional By default, the data-type is inferred from the input data. order : {‘C’, ‘F’}, optional Whether to use row-major (‘C’) or column-major (‘F’ for FORTRAN) memory representation. Defaults to ‘C’. out : ndarray Array interpretation of a. No copy is performed if the input is already an ndarray. If a is a subclass of ndarray, a base class ndarray is returned.

See also

asanyarray
Similar function which passes through subclasses.
ascontiguousarray
Convert input to a contiguous array.
asfarray
Convert input to a floating point ndarray.
asfortranarray
Convert input to an ndarray with column-major memory order.
asarray_chkfinite
Similar function which checks input for NaNs and Infs.
fromiter
Create an array from an iterator.
fromfunction
Construct an array by executing a function on grid positions.

Examples

Convert a list into an array:

```>>> a = [1, 2]
>>> np.asarray(a)
array([1, 2])
```

Existing arrays are not copied:

```>>> a = np.array([1, 2])
>>> np.asarray(a) is a
True
```

If dtype is set, array is copied only if dtype does not match:

```>>> a = np.array([1, 2], dtype=np.float32)
>>> np.asarray(a, dtype=np.float32) is a
True
>>> np.asarray(a, dtype=np.float64) is a
False
```

Contrary to asanyarray, ndarray subclasses are not passed through:

```>>> issubclass(np.matrix, np.ndarray)
True
>>> a = np.matrix([[1, 2]])
>>> np.asarray(a) is a
False
>>> np.asanyarray(a) is a
True
```

numpy.squeeze

numpy.asanyarray