{"id":36944,"date":"2022-11-19T09:48:28","date_gmt":"2022-11-19T09:48:28","guid":{"rendered":"https:\/\/www.askpython.com\/?p=36944"},"modified":"2023-02-16T19:56:34","modified_gmt":"2023-02-16T19:56:34","slug":"numpy-zeros_like","status":"publish","type":"post","link":"https:\/\/www.askpython.com\/python-modules\/numpy\/numpy-zeros_like","title":{"rendered":"NumPy zeros_like – A Complete Guide"},"content":{"rendered":"\n

In this tutorial, we will be learning about the NumPy zeros_like method and also seeing a lot of examples regarding the same. So let us begin!<\/p>\n\n\n\n

Recommended Read: NumPy zeros – A Complete Guide<\/a><\/em><\/strong><\/p>\n\n\n\n

\n\n\n\n

What is NumPy zeros_like?<\/h2>\n\n\n\n

The zeros_like<\/code> method in NumPy is a function that returns an array of zeros having the same shape and size as the given array.<\/p>\n\n\n\n

\n\n\n\n

Syntax of zeros_like<\/h2>\n\n\n\n

numpy.<\/strong>zeros_like<\/strong>(<\/strong>a<\/em>, <\/strong>dtype=None<\/em>, <\/strong>order=’K’<\/em>, <\/strong>subok=True<\/em>, <\/strong>shape=None<\/em>)<\/strong><\/p>\n\n\n\n

Parameter<\/strong><\/td>Description<\/strong><\/td>Required\/Optional<\/strong><\/td><\/tr>
a (array_like)<\/td>An object which defines the shape and data type of the array to be returned.<\/td>Required<\/td><\/tr>
dtype (data type)<\/td>The data type of the desired array. Overrides the data type of the result.<\/td>Optional<\/td><\/tr>
order<\/td>The desired order in which the multi-dimensional data is to be stored in the memory. It can be row-major (\u2018C\u2019), column-major (\u2018F\u2019), ‘A’ means ‘F’ if a<\/em> is Fortran contiguous, ‘C’ otherwise. ‘K’ implies matching the layout of a<\/em> as much as possible.<\/td>Optional<\/td><\/tr>
subok (bool)<\/td>Determines whether the newly created array will use the sub-class type of a<\/em> (subok=True) or will be a base class array (subok=False).
The default value is True<\/strong>.<\/td>		Optional<\/td><\/tr>
shape<\/td>The shape of the desired array. Overrides the shape of the result. <\/td>Optional<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Returns:<\/strong>
An array with the same shape and data type as the given array.<\/p>\n\n\n\n

\n\n\n\n

Examples of Numpy zeros_like function<\/h2>\n\n\n\n

Let’s now take a look at how the numpy.zeros_like() function works and what is the expected output for different types of inputs.<\/p>\n\n\n\n

1-dimensional array using zeros_like <\/h3>\n\n\n
\nimport numpy as np\n\na = np.arange(10)\nprint("a =", a)\n\nb = np.zeros_like(a)\nprint("b =", b)\n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\na = [0 1 2 3 4 5 6 7 8 9]\nb = [0 0 0 0 0 0 0 0 0 0]\n<\/pre><\/div>\n\n\n
\n\n\n\n
2-dimensional array using zeros_like<\/h3>\n\n\n\n
N x N array<\/strong><\/p>\n\n\n
\nimport numpy as np\n\na = np.arange(10).reshape(2, 5)\nprint("a =\\n", a)\n\nb = np.zeros_like(a)\nprint("b =\\n", b)\n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\na =\n [[0 1 2 3 4]\n [5 6 7 8 9]]\nb =\n [[0 0 0 0 0]\n [0 0 0 0 0]]\n<\/pre><\/div>\n\n\n
1 x N array<\/strong><\/p>\n\n\n
\nimport numpy as np\n\na = np.arange(10).reshape(1, 10)\nprint("a =\\n", a)\n\nb = np.zeros_like(a)\nprint("b =\\n", b)\n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\na =\n [[0 1 2 3 4 5 6 7 8 9]]\nb =\n [[0 0 0 0 0 0 0 0 0 0]]\n<\/pre><\/div>\n\n\n
N x 1 array<\/strong><\/p>\n\n\n
\nimport numpy as np\n\na = np.arange(10).reshape(10, 1)\nprint("a =\\n", a)\n\nb = np.zeros_like(a)\nprint("b =\\n", b)\n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\na =\n [[0]\n [1]\n [2]\n [3]\n [4]\n [5]\n [6]\n [7]\n [8]\n [9]]\nb =\n [[0]\n [0]\n [0]\n [0]\n [0]\n [0]\n [0]\n [0]\n [0]\n [0]]\n<\/pre><\/div>\n\n\n
\n\n\n\n
1-dimensional float-type array<\/h3>\n\n\n
\nimport numpy as np\n\na = np.arange(10)\nprint("a =", a)\n\nb = np.zeros_like(a, dtype=float)\nprint("b =", b)\n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\na = [0 1 2 3 4 5 6 7 8 9]\nb = [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n<\/pre><\/div>\n\n\n
\n\n\n\n
2-dimensional float-type array<\/h3>\n\n\n
\nimport numpy as np\n\na = np.arange(10).reshape(2, 5)\nprint("a =\\n", a)\n\nb = np.zeros_like(a, dtype=float)\nprint("b =\\n", b)\n<\/pre><\/div>\n\n\n
Output:<\/strong><\/p>\n\n\n
\na =\n [[0 1 2 3 4]\n [5 6 7 8 9]]\nb =\n [[0. 0. 0. 0. 0.]\n [0. 0. 0. 0. 0.]]\n<\/pre><\/div>\n\n\n
\n\n\n\n
Difference between zeros and zeros_like<\/h2>\n\n\n\n
Note that, in the zeros<\/code> method we are creating a new array of our desired shape and data type having all the values as 0. But, here, we are directly passing an array or an array-like object to get an array of the same shape and data type. The NumPy zeros_like<\/code> function takes more time than the NumPy zeros<\/code> function to produce an array with all 0.<\/p>\n\n\n\n
\n\n\n\n
Conclusion<\/h2>\n\n\n\n
That\u2019s all! In this tutorial, we learned about the\u00a0Numpy zeros_like<\/strong>\u00a0method and practiced different types of examples using the same.<\/p>\n\n\n\n
\n\n\n\n
Reference<\/h2>\n\n\n\n