W3Schools
w3schools.com › python › numpy › numpy_array_shape.asp
NumPy Array Shape
NumPy arrays have an attribute called shape that returns a tuple with each index having the number of corresponding elements.
NumPy
numpy.org › devdocs › reference › generated › numpy.shape.html
numpy.shape — NumPy v2.5.dev0 Manual
The elements of the shape tuple give the lengths of the corresponding array dimensions. ... Equivalent array method. ... Try it in your browser! >>> import numpy as np >>> np.shape(np.eye(3)) (3, 3) >>> np.shape([[1, 3]]) (1, 2) >>> np.shape([0]) (1,) >>> np.shape(0) ()
Videos
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Learn to reshape NumPy arrays in 4 minutes! ↔️ - YouTube
NumPy
numpy.org › doc › 2.3 › reference › generated › numpy.shape.html
numpy.shape — NumPy v2.3 Manual
The elements of the shape tuple give the lengths of the corresponding array dimensions. ... Equivalent array method. ... Try it in your browser! >>> import numpy as np >>> np.shape(np.eye(3)) (3, 3) >>> np.shape([[1, 3]]) (1, 2) >>> np.shape([0]) (1,) >>> np.shape(0) ()
Note.nkmk.me
note.nkmk.me › home › python › numpy
NumPy: Get the dimensions, shape, and size of an array | note.nkmk.me
April 23, 2025 - NumPy: Add new dimensions to an array (np.newaxis, np.expand_dims) The shape of a NumPy array, i.e., the length of each dimension, is represented as a tuple and can be accessed using the shape attribute.
NumPy
numpy.org › doc › 2.1 › reference › generated › numpy.shape.html
numpy.shape — NumPy v2.1 Manual
The elements of the shape tuple give the lengths of the corresponding array dimensions. ... Equivalent array method. ... >>> import numpy as np >>> np.shape(np.eye(3)) (3, 3) >>> np.shape([[1, 3]]) (1, 2) >>> np.shape([0]) (1,) >>> np.shape(0) ()
Vultr Docs
docs.vultr.com › python › third-party › numpy › shape
Python Numpy shape() - Get Array Dimensions | Vultr Docs
November 19, 2024 - The shape() function in NumPy is an essential tool for managing and manipulating multidimensional arrays. It provides insights into the dimensions of an array, which is crucial when performing array operations that require an understanding of ...
NumPy
numpy.org › doc › 2.2 › reference › generated › numpy.ndarray.shape.html
numpy.ndarray.shape — NumPy v2.2 Manual
The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it. As with numpy.reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of ...
NumPy
numpy.org › doc › stable › reference › generated › numpy.shape.html
numpy.shape — NumPy v2.4 Manual
The elements of the shape tuple give the lengths of the corresponding array dimensions. ... Equivalent array method. ... Try it in your browser! >>> import numpy as np >>> np.shape(np.eye(3)) (3, 3) >>> np.shape([[1, 3]]) (1, 2) >>> np.shape([0]) (1,) >>> np.shape(0) ()
Programiz
programiz.com › python-programming › numpy › methods › shape
NumPy shape()
The shape() method returns the shape of an array i.e. the number of elements in each dimension. import numpy as np array = np.array([[0, 1], [2, 3]])
GeeksforGeeks
geeksforgeeks.org › python › numpy-array-shape
NumPy Array Shape - GeeksforGeeks
July 15, 2025 - The shape of an array can be defined as the number of elements in each dimension. Dimension is the number of indices or subscripts, that we require in order to specify an individual element of an array.
Medium
medium.com › @amit25173 › understanding-numpy-shape-6fbb6b83891e
Understanding numpy.shape. If you think you need to spend $2,000… | by Amit Yadav | Medium
February 9, 2025 - For a 1D array, the shape might look like (n,), where n is the number of elements. For a 3D array, it could look like (x, y, z), representing depth, rows, and columns. Here’s another quick example to make it more fun. You might wonder: “What if I have a 3D array?” Let’s explore that: # Creating a 3D NumPy array array_3d = np.array([ [[1, 2], [3, 4]], [[5, 6], [7, 8]] ]) print("3D Array Shape:", array_3d.shape)
NumPy
numpy.org › devdocs › reference › generated › numpy.ndarray.shape.html
numpy.ndarray.shape — NumPy v2.5.dev0 Manual
The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it. As with numpy.reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of ...
Codecademy
codecademy.com › docs › python:numpy › ndarray › .shape
Python:NumPy | ndarray | .shape | Codecademy
October 30, 2025 - The .shape attribute of a NumPy ndarray returns a tuple of integers specifying the size of the array in each dimension.
NumPy
numpy.org › doc › stable › reference › generated › numpy.ndarray.shape.html
numpy.ndarray.shape — NumPy v2.4 Manual
The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it. As with numpy.reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of ...
Top answer 1 of 2
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This is just notation - in Python, tuples are distinguished from expression grouping (or order of operations stuff) by the use of commas - that is, (1,2,3) is a tuple and (2x + 4) ** 5 contains an expression 2x + 4. In order to keep single-element tuples distinct from single-element expressions, which would otherwise be ambiguous ((1) vs (1) - which is the single-element tuple and which a simple expression that evaluates to 1?), we use a trailing comma to denote tuple-ness.
What you're getting is a single dimension response, since there's only one dimension to measure, packed into a tuple type.
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Numpy supports not only 2-dimensional arrays, but multi-dimensional arrays, and by multi-dimension I mean 1-D, 2-D, 3-D .... n-D, And there is a format for representing respective dimension array. The len of array.shape would get you the number of dimensions of that array. If the array is 1-D, the there is no need to represent as (m, n) or if the array is 3-D then it (m, n) would not be sufficient to represent its dimensions.
So the output of array.shape would not always be in (m, n) format, it would depend upon the array itself and you will get different outputs for different dimensions.
NumPy
numpy.org › doc › 2.2 › reference › generated › numpy.shape.html
numpy.shape — NumPy v2.2 Manual
The elements of the shape tuple give the lengths of the corresponding array dimensions. ... Equivalent array method. ... >>> import numpy as np >>> np.shape(np.eye(3)) (3, 3) >>> np.shape([[1, 3]]) (1, 2) >>> np.shape([0]) (1,) >>> np.shape(0) ()
Python Guides
pythonguides.com › python-numpy-shape
NumPy Shape And Array Dimensions In Python
May 16, 2025 - The shape attribute returns a tuple showing the size of each dimension. For a 1D array, it’s simply the number of elements. For a 2D array, it’s (rows, columns). For higher dimensions, it follows the same pattern. ... Let’s use a more practical example.
Medium
medium.com › @amit25173 › numpy-shape-in-python-6cfe7cb20084
numpy.shape in Python. If you think you need to spend $2,000… | by Amit Yadav | Medium
February 9, 2025 - Think of it like this: if your data is stored in rows and columns, the shape of the array is like its blueprint. In NumPy, the shape attribute tells you the dimensions of your array—whether it’s a single row, a grid of numbers, or something ...
NumPy
numpy.org › doc › 2.1 › reference › generated › numpy.ndarray.shape.html
numpy.ndarray.shape — NumPy v2.1 Manual
The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it. As with numpy.reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of ...
Reddit
reddit.com › r/learnpython › what's the difference between a numpy array with shape (x,) and (x, 1)?
r/learnpython on Reddit: what's the difference between a numpy array with shape (x,) and (x, 1)?
October 6, 2023 -
hey, im doing an ai thing in school and my code didnt work as expected, and after 5 hours i found out i reshaped an array from (206,) to (206,1) and that made the results wrong. and from what i understand, the shape means the length of each dimension, and length is not 0 indexed so a size of 1 would be equal to just 1D no?
Top answer 1 of 4
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import numpy as np arr = np.array([1, 2, 3, 4, 5]) print(arr.shape) # Output: (5,) arr = np.array([[1], [2], [3], [4], [5]]) print(arr.shape) # Output: (5, 1) arr = np.array([[1, 2, 3, 4, 5]]) print(arr.shape) # Output: (1, 5)
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Collections such as tuples, lists and 1d ndarrays are neither rows or columns (generally they display using whatever is most convenient) which si why: archive = ('hello', 'hello', 'hello') Can be rewritten as: archive = ('hello', 'hello', 'hello') And the two expressions are equivalent. For a ndarray the dimension of a vector is important for some applications: import numpy as np vector = np.array([0, 1, 2]) row = vector[np.newaxis, :] col = vector[:, np.newaxis] The vector: vector.shape vector.ndim Is a single dimension: (3, ) 1 The following are 2 dimensions: row.shape row.ndim (1, 3) 2 This is by definition 1 row by multiple (in this case 3) columns. col.shape cold.ndim (3, 1) 2 This is by definition multiple (in this case 3) rows by 1 column. For matrix multiplication for example, the inner dimensions need to match row @ col #(1, 3) @ (3, 1) And the array returned has the outer dimensions: array([[5]]) #(1) @ (1) If the other way is compared: col @ row #(3, 1) @ (1, 3) array([[0, 0, 0], [0, 1, 2], [0, 2, 4]]) #(3) @ (3)