Numpy.empty_like() method “returns a new array with the same shape and type as a given array”.
Syntax
numpy.empty_like(shape, order, dtype, subok)
Parameters
It takes four parameters, out of which 2 parameters are optional.
- shape: It represents the number of rows.
- order: It represents the order in the memory. (C_contiguous or F_contiguous).
- dtype: It’s the data type, and it is optional and has a float value by default.
- bool: It checks if we have to create the sub-class of the main array or not.
Return Value
It returns the ndarray of the same shape and size.
Example 1: How does the np.empty_like() method work
import numpy as np
data = ([11, 21, 31], [41, 51, 61])
res = np.empty_like(data, dtype=int)
print("\nMatrix a : \n", res)Output
Matrix a :
[[0 0 0]
[0 0 0]]Example 2: How to Use the numpy empty_like() method
import numpy as np
arr = np.empty_like([4, 4], dtype=int)
print("\nMatrix arr : \n", arr)
mArr = arr = ([12, 23, 43, 33], [46, 15, 61, 1], [3, 4, 6,7],
[66, 31, 35, 73])
print("\nMatrix mArr : \n", np.empty_like(mArr))
Output
Matrix arr :
[2854797676290708764 8240043042726748728]
Matrix mArr :
[[0 0 0 0]
[0 0 0 0]
[0 0 0 0]
[0 0 0 0]]
That’s it!
Ankit Lathiya is a Master of Computer Application by education and Android and Laravel Developer by profession and one of the authors of this blog. He is also expert in JavaScript and Python development.
