numpy.reshape numpy.reshape(a, newshape, order='C') [source] Donne une nouvelle forme à un tableau sans changer ses données. You can use the reshape function for this. # importing the numpy module import numpy as np arr = np.arange(8) print(arr.reshape(2, 4).base) Output [0 1 2 3 4 5 6 7] You can see that it returns the original array. In the case of reshaping a one-dimensional array into a two-dimensional array with one column, the tuple would be the shape of the array as the first dimension (data.shape[0]) and 1 for the second dimension. Passing Unknown Dimension. In [1]: np.reshape(arrayA, (2, 4), order='F') Out[1]: array([[0, 2, 4, 6], [1, 3, 5, 7]]) La valeur par défaut de ordre est C, ce qui signifie que les données sont lues ou écrites dans un ordre d’index de type C, ou en mots simples, dans l’ordre des lignes. In numpy reshape(), you are allowed to have one “unknown” dimension. numpy.reshape. Paramètres: a : array_like Tableau à refaçonner. The syntax is numpy.reshape(a, newShape, order='C') Here, a: Array that you w A numpy matrix can be reshaped into a vector using reshape function with parameter -1. Cependant, je ne pense pas que ce soit une bonne idée d'utiliser un code comme celui-ci. But I don't know what -1 means here. b = numpy.reshape(a, -1) Il appellera des opérations de deafult à la matrice a, qui renvoie une 1- d numpy array/martrix. And numpy … The numpy.reshape() function is available in NumPy package. newshape : int ou tuple d'ints La nouvelle forme doit être compatible avec la forme d'origine. The numpy.reshape() function helps us to get a new shape to an array without changing its data. It simply means that it is an unknown dimension and we want numpy to figure it out. S'il s'agit d'un entier, le résultat sera un tableau 1-D de cette longueur. As the name suggests, reshape means 'changes in shape'. Interesting observation that: b.reshape(1,4).strides -> (32, 8) Here's my guess.

The reshape() function takes a single argument that specifies the new shape of the array. Reshape Data In some occasions, you need to reshape the data from wide to long. NumPy provides the reshape() function on the NumPy array object that can be used to reshape the data. In this case, the value is inferred from the length of the array and remaining dimensions. numpy allow us to give one of new shape parameter as -1 (eg: (2,-1) or (-1,3) but not (-1, -1)). Pourquoi ne pas essayer: b = a.reshape(1,-1) Il vous donnera le même résultat et il est plus clair pour les lecteurs à comprendre: b Définir comme une autre forme de. If an integer, then the result will be a 1-D array of that length. Numpy reshape() method returns the original array, so it returns a view. So it is a view. One shape dimension can be … .__array_interface__ is displaying an underlying attribute, and .strides is more like a property (though it may all be buried in C code). The default underlying value is None, and when needed for calculation (or display with .strides) it calculates it from the shape and item size. One shape dimension can be -1.