empty_like is a method of NumPy that returns a new array with same shape and type as the provided array. In this post, I will show you how to use empty_like method with examples.
empty_like is defined as like below:
empty_like(p, dtype=None, order='K', subok=True, shape=None)
- p is the prototype, the data-type and shape define the same attributes of the returning array. It is an array_like parameter.
- dtype is a data-type optional parameter. It is used to override the data type of the result array.
- order is an optional parameter that overrides the memory layout of the result array. It can be ‘C’, ‘F’, ‘A’ or ‘K’.
- subok is an optional boolean value. For False, the result array will be base-class array. If True, it will use the sub class type of prototype.
- shape is an optional value, it can be int or sequence of ints. It overrides the shape of the result array.
Let’s try this with an example:
import numpy as np given_arr = ([1,2],[3,4], [5,6]) print(np.empty_like(given_arr))
It will print one output as like below:
[[-4611686018427387904 -4611686018427387904] [ 4616981938510757898 4613349226564724111] [-4611686018427387904 -4611686018427387904]]
It created an uninitialized array.
Each time you run this program, it will create one different uninitialized array.
Let’s change the data type of the returned array to string:
import numpy as np given_arr = ([1,2],[3,4], [5,6]) print(np.empty_like(given_arr, dtype=str))
It will give:
[['' ''] ['' ''] ['' '']]
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