try ndarray.flatten(array), that way you are dealing with a one dim array which can be manipulated with numpy.put(array,[indices],[values]). Then use ndarray.reshape() to get to the original dimensions.

Jan 24, 2025 · Array Indexing in NumPy is used to access or modify specific elements of an array. It allows to retrieve data from arrays by specifying the positions (indices) of elements. This is the important feature in NumPy that enables efficient data manipulation and analysis for numerical computing tasks.

May 6, 2013 · TL;DR: Use advanced indexing: b[*a.T] = 10. You can also transpose the index array a, convert the result into a tuple and index the array b and assign a value. Converting the index array into a tuple (or unpacking it inside a []) ensures that multidimensional indexing works as …

Aug 20, 2019 · I have a Numpy 2D-array a and a list of indices list_indices. I want to modify the elements of the array a at each index specified in the list_indices. The current approach I am using is with for-loop. For example, if I want to change the sign of those elements: for index in list_indices: a[index[0],index[1]] = -np.sign(a[index[0],index[1]])

ndarrays can be indexed using the standard Python x[obj] syntax, where x is the array and obj the selection. There are different kinds of indexing available depending on obj: basic indexing, advanced indexing and field access. Most of the following examples show the use of indexing when referencing data in an array.

Indexing in numpy arrays is a critical feature that allows you to access, modify, and manipulate specific elements, rows, columns, or a subarray within a larger array. This article will explore various methods and techniques of indexing in numpy arrays, providing detailed examples to illustrate each concept. 1. Basic Indexing

Mar 26, 2025 · Learn how to create a 2D NumPy array and use boolean indexing to replace elements that meet a certain condition with a specified value. Follow our step-by-step guide.

Common Practices of Numpy Array Indexing. Selecting Elements Based on Conditions; Modifying Subsets of Arrays; Indexing Multi-Dimensional Arrays; Best Practices of Numpy Array Indexing. Using Views Instead of Copies; Avoiding Unnecessary Indexing Operations; Testing Indexing Operations; Conclusion; References; Fundamental Concepts of Numpy ...

You can use integer array indexing to construct arrays by indexing with other arrays. Example in numpy code row_indices = np.array([ 1 , 0 , 2 ]) column_indices = np.array([ 2 , 1 , 0 ]) # Select elements based on the indices arrays selected_elements = arr_2d[row_indices, column_indices] print (selected_elements) #Outputs: [6 2 7]

Aug 7, 2014 · You could use np.ix_ to construct the desired index arrays: In [91]: S[np.ix_([i,j],[i,j])] Out[91]: array([[1, 0], [0, 3]]) In [92]: tmp_arr = np.eye(2)*555 In [93]: tmp_arr Out[93]: array([[ 555., 0.], [ 0., 555.]])