import numpy as np x = np.array([2,5,1,9,0,3,8,11,-4,-3,-8,6,10]) Basic Indexing. Dans le cas 1d, il retourne result = ary[newaxis,:]. The value -1 is special for the reshape method. ar.reshape(ar.shape[0],-1) est un moyen astucieux de contourner l' if test. 4,440 1 1 gold badge 17 17 silver badges 36 36 bronze badges. numpy.reshape numpy.reshape(a, newshape, order='C') [source] Donne une nouvelle forme à un tableau sans changer ses données. Ilse Ilse. In this article we will discuss different ways to convert a 2D numpy array or Matrix to a 1D Numpy Array. Understanding Numpy reshape() Python numpy.reshape(array, shape, order = ‘C’) function shapes an array without changing data of array. It means, “make a dimension the size that will use the remaining unspecified elements”. Vous pouvez également jouer avec l'ordre dans lequel les numéros sont tirés dans B utilisant le mot-clé order. Before jumping to numpy.reshape() we have to understand how these arrays are stored in the memory and what is a contiguous and non-contiguous arrays . 2D array are also called as Matrices which can be represented as collection of rows and columns.. A contiguous array is just an array stored in an unbroken block of memory and to access the next value in the array, we just move to the next memory address. Convert 2D Numpy array / Matrix to a 1D Numpy array using flatten() Python’s Numpy module provides a member function in ndarray to flatten its contents i.e.