December 16, 2022 - The ones_like method in NumPy is a function that returns an array of ones having the same shape and size as the given array.

full_like · Return a new array with shape of input filled with value. ones · Return a new array setting values to one. Examples · Try it in your browser! >>> import numpy as np >>> x = np.arange(6) >>> x = x.reshape((2, 3)) >>> x array([[0, 1, 2], [3, 4, 5]]) >>> np.ones_like(x) array([[1, 1, 1], [1, 1, 1]]) >>> y = np.arange(3, dtype=np.float64) >>> y array([0., 1., 2.]) >>> np.ones_like(y) array([1., 1., 1.]) Go BackOpen In Tab ·

April 21, 2026 - NumPy array creation: numpy.ones_like() function, example - Return an array of ones with the same shape and type as a given array.

This example generates a 3x4x2 array filled with ones, using column-major order for memory layout. import numpy as np existing_array = np.array([[1, 2], [3, 4]]) array = np.ones_like(existing_array) Powered by

May 15, 2011 - numpy.ones_like(x[, out])¶ · ... See also · zeros_like, ones · Examples · >>> a = np.array([[1, 2, 3], [4, 5, 6]]) >>> np.ones_like(a) array([[1, 1, 1], [1, 1, 1]]) numpy.ones ·...

full_like · Return a new array with shape of input filled with value. ones · Return a new array setting values to one. Examples · >>> import numpy as np >>> x = np.arange(6) >>> x = x.reshape((2, 3)) >>> x array([[0, 1, 2], [3, 4, 5]]) >>> np.ones_like(x) array([[1, 1, 1], [1, 1, 1]]) >>> y = np.arange(3, dtype=float) >>> y array([0., 1., 2.]) >>> np.ones_like(y) array([1., 1., 1.]) On this page

full_like · Return a new array with shape of input filled with value. ones · Return a new array setting values to one. Examples · Try it in your browser! >>> import numpy as np >>> x = np.arange(6) >>> x = x.reshape((2, 3)) >>> x array([[0, 1, 2], [3, 4, 5]]) >>> np.ones_like(x) array([[1, 1, 1], [1, 1, 1]]) >>> y = np.arange(3, dtype=np.float64) >>> y array([0., 1., 2.]) >>> np.ones_like(y) array([1., 1., 1.]) Go BackOpen In Tab ·

This is useful for initializing arrays with the same dimensions and data type as a reference array. In this example, we create a 3D array of ones with the shape of a 3x3x3 array −

full_like · Return a new array with shape of input filled with value. ones · Return a new array setting values to one. Examples · >>> import numpy as np >>> x = np.arange(6) >>> x = x.reshape((2, 3)) >>> x array([[0, 1, 2], [3, 4, 5]]) >>> np.ones_like(x) array([[1, 1, 1], [1, 1, 1]]) >>> y = np.arange(3, dtype=float) >>> y array([0., 1., 2.]) >>> np.ones_like(y) array([1., 1., 1.]) On this page

February 18, 2020 - >>> x = np.arange(6) >>> x = x.reshape((2, 3)) >>> x array([[0, 1, 2], [3, 4, 5]]) >>> np.ones_like(x) array([[1, 1, 1], [1, 1, 1]])

>>> x = np.arange(6) >>> x = x.reshape((2, 3)) >>> x array([[0, 1, 2], [3, 4, 5]]) >>> np.ones_like(x) array([[1, 1, 1], [1, 1, 1]])

>>> import numpy as np >>> np.ones(5) array([1., 1., 1., 1., 1.])

full_like · Return a new array with shape of input filled with value. ones · Return a new array setting values to one. Examples · Try it in your browser! >>> import numpy as np >>> x = np.arange(6) >>> x = x.reshape((2, 3)) >>> x array([[0, 1, 2], [3, 4, 5]]) >>> np.ones_like(x) array([[1, 1, 1], [1, 1, 1]]) >>> y = np.arange(3, dtype=float) >>> y array([0., 1., 2.]) >>> np.ones_like(y) array([1., 1., 1.]) Go BackOpen In Tab ·

May 29, 2016 - >>> x = np.arange(6) >>> x = x.reshape((2, 3)) >>> x array([[0, 1, 2], [3, 4, 5]]) >>> np.ones_like(x) array([[1, 1, 1], [1, 1, 1]])

June 15, 2026 - Explanation: np.ones((3, 4)) creates a 2D array with 3 rows and 4 columns filled with 1. Example 2: This example creates an array of integers instead of the default floating-point values.

April 21, 2026 - Example: Create a 2D array with a custom data type and byte order ... import numpy as np # Create a 2D array of unsigned 16-bit integers with little-endian byte order nums = np.ones((2, 3), dtype=np.dtype('<u2')) print(nums)

June 10, 2016 - @attr.gpu @condition.retry(3) def test_backward_negative_axis_gpu(self): for i in range(self.x.ndim): gy = numpy.ones_like(self.x.sum(axis=-1)) * self.gy self.check_backward(cuda.to_gpu(self.x), cuda.to_gpu(gy), axis=-1)

March 8, 2024 - # Python Programming illustrating # numpy.ones_like method import numpy as geek array = geek.arange(10).reshape(5, 2) print("Original array : \n", array) b = geek.ones_like(array, float) print("\nMatrix b : \n", b) array = geek.arange(8) c = geek.ones_like(array) print("\nMatrix c : \n", c) Output:

January 23, 2024 - 1. 1.]] print(np.ones_like(a_float64).dtype) # float64

The numpy.ones_like() function returns an array of ones, 1, with the same shape and types as the array_like that is passed to it.