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1. stackoverflow.com › questions › 18691084python - What does -1 mean in numpy reshape? - Stack Overflow

   stackoverflow.com › questions › 18691084

   Sep 9, 2013 · It is quite simple, -1 means 'whatever it takes' to flatten. So, in the above example, a.reshape (2,-1) would mean 2*4, a.reshape (4,-1) would mean 4*2, a.reshape (2,2,-1) would mean 2,2,2 and just a.reshape (-1) would mean 8. – rishi jain. Sep 21, 2022 at 23:46.

2. www.geeksforgeeks.org › numpy-reshape-pythonnumpy.reshape() in Python - GeeksforGeeks

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   Mar 8, 2024 · The numpy.reshape () function shapes an array without changing the data of the array. Syntax: numpy.reshape(array, shape, order = 'C') Parameters : array : [array_like]Input array. shape : [int or tuples of int] e.g. if we are arranging an array with 10 elements then shaping.

3. saturncloud.io › blog › understanding-theUnderstanding the Differences Between Numpy Reshape(-1, 1 ...

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   Sep 23, 2023 · The key difference between reshape(-1, 1) and reshape(1, -1) is the shape of the resulting array. reshape(-1, 1) results in an array with a single column and multiple rows (a column vector), while reshape(1, -1) results in an array with a single row and multiple columns (a row vector).

4. www.machinelearningplus.com › python › numpy-reshapeNumpy Reshape – How to reshape arrays and what does -1 mean?

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   The numpy.reshape () function is used to reshape a numpy array without changing the data in the array. It is a very common practice to reshape arrays to make them compatible for further calculations. In this article, you will learn about the possible use cases of the numpy.reshape function.

5. numpy.org › reference › generatednumpy.reshape — NumPy v2.1 Manual

   numpy.org › reference › generated

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   Gives a new shape to an array without changing its data. Parameters: aarray_like. Array to be reshaped. shapeint or tuple of ints. The new shape should be compatible with the original shape. If an integer, then the result will be a 1-D array of that length. One shape dimension can be -1.

6. realpython.com › numpy-reshapeUsing NumPy reshape() to Change the Shape of an Array

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   NumPy’s reshape() allows you to change the shape of the array without changing its data. You can reshape an array into a different configuration with either the same or a different number of dimensions. In the following sections, you’ll work through several short examples that use reshape() to convert arrays from one shape into another.

7. People also ask

   What is reshape() in NumPy?

   • Numpy is a powerful library in Python that provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays. One of the most commonly used functions in Numpy is reshape(), which gives a new shape to an array without changing its data.

   Understanding the Differences Between Numpy Reshape(-1, 1) and Reshape

   saturncloud.io/blog/understanding-the-differences-between-numpy-reshape1-1-and-reshape1-1/

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   What is the difference between reshape(1 -1) & re-shaping a 2D array?

   • On the other hand, reshape(1, -1) reshapes your array with 1 row and as many columns as necessary to accommodate the data. This operation will result in a 2D array with a shape (1, n), where n is the number of elements in your original array. Here’s an example: Output:

   Understanding the Differences Between Numpy Reshape(-1, 1) and Reshape

   saturncloud.io/blog/understanding-the-differences-between-numpy-reshape1-1-and-reshape1-1/

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   How to reshape a 2D array using NumPy?

   • When you use reshape(-1, 1), you are asking numpy to reshape your array with 1 column and as many rows as necessary to accommodate the data. This operation will result in a 2D array with a shape (n, 1), where n is the number of elements in your original array. Here’s an example: Output:

   Understanding the Differences Between Numpy Reshape(-1, 1) and Reshape

   saturncloud.io/blog/understanding-the-differences-between-numpy-reshape1-1-and-reshape1-1/

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   How to use reshape()?

   • You can use reshape() as either a function or a method. The documentation for the function shows three parameters: a is the original array. newshape is a tuple or an integer with the shape of the new array. When newshape is an integer, the new array will have one dimension. order enables you to determine how the data is configured in the new array.

   Using NumPy reshape() to Change the Shape of an Array

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8. note.nkmk.me › en › python-numpy-reshape-usageNumPy: reshape () to change the shape of an array - nkmk note

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   Feb 1, 2024 · Contents. How to use the reshape() method of ndarray. How to use the np.reshape() function. Specify the conversion order: order. The meaning of -1 in reshape() reshape() returns a view. To check the shape and the number of dimensions of ndarray, refer to the following article. NumPy: Get the number of dimensions, shape, and size of ndarray.