Copies and bindings between python objects

Copies and bindings between python objects

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Given a matrix, A of size M x N of 0s and 1s. If an element is 0, set its entire row and column to 0.

Input 1:
    [   [1, 0, 1],
        [1, 1, 1], 
        [1, 1, 1]   ]

Output 1:
    [   [0, 0, 0],
        [1, 0, 1],
        [1, 0, 1]   ]

My Code:

def setZeroes(self, A):
    B=A
    for i in range(len(A)):
        for j in range(len(A[i])):
            if(A[i][j]==0):
                for x in range(len(A[i])):
                    B[i][x]=0
                    for y in range(len(A)):
                        B[y][j]=0
        A=B
    return A

Is creating a zero matrix. What do I miss ?


import copy

def setZeroes(A):
   B=copy.deepcopy(A)
   for i in range(len(A)):
       for j in range(len(A[i])):
           if(A[i][j]==0):
               for x in range(len(A[i])):
                   B[i][x]=0
               for y in range(len(A)):
                   B[y][j]=0
   return B

A =  [   [1, 0, 1],
        [1, 1, 1], 
        [1, 1, 1]   ]

print(setZeroes(A))

since, array is pass by reference, you have to deepcopy it.

copy — Shallow and deep copy operations, Assignment statements in Python do not copy objects, they create bindings between a target and an object. For collections that are mutable or contain mutable  copy in Python (Deep Copy and Shallow Copy) In Python, Assignment statements do not copy objects, they create bindings between a target and an object. When we use = operator user thinks that this creates a new object; well, it doesn’t. It only creates a new variable that shares the reference of the original object.


You can do it by masking rows and columns with a zero

mask = A==0
A[:, mask.any(0)] = 0
A[mask.any(1)] = 0
print(A)
array([[0, 0, 0],
       [1, 0, 1],
       [1, 0, 1]])

Shallow vs Deep Copying of Python Objects – Real Python, What's the difference between a shallow and a deep copy of a Python object? in Python do not create copies of objects, they only bind names to an object. Assignment statements in Python do not copy objects, they create bindings between a target and an object. For collections that are mutable or contain mutable items, a copy is sometimes needed so one can change one copy without changing the other. This module provides generic shallow and deep copy operations (explained below).


def func(l):    
    r = []
    c = []

    # storing the index value of coloum and row where there is 0 everywhere
    for i,v1 in enumerate(l):
        for j,v2 in enumerate(v1):
            if v2 ==0:
                c.append(j)
                r.append(i)
    r = list(set(r))
    c = list(set(c))

    # making all element of row 0
    for i in r:
        for j in range(len(l[i])):
            l[i][j] = 0

    # making lal element of coloumn 0
    for i in c:
        for j in range(len(l)):
            l[j][i] = 0
    return l


l = [   [1, 0, 1],
        [1, 1, 1], 
        [1, 1, 1]   ]

res = func(l)

print(res,sep='\n')

output:

[
 [0, 0, 0],
  [1, 0, 1],
  [1, 0, 1]
]

copy in Python (Deep Copy and Shallow Copy), It says in the Python documentation that “Assignment statements in Python do not copy objects, they create bindings between a target and an  To build your Python bindings with Cython, you’ll follow similar steps to those you used for CFFI and PyBind11. You’ll write the bindings, build them, and then run Python code to call them. Cython can support both C and C++. For this example, you’ll use the cppmult library that you used for the PyBind11 example above. Write the Bindings


Your problem is right here:

B=A

This doesn't create three new arrays for the rows of B: it makes B point to the same arrays as A. So as soon as you set the first row and column to zeros, there are additional zeroes which your loop discovers. Copy each row (or deep copy) to avoid the problem.

Assignment, Shallow Copy, Or Deep Copy?, exception copy.error: It raises for module specific errors. - Assignment statements in Python do not copy objects and they create bindings between a target and  2. Python Copy Module. When we perform an assignment in Python, it does not copy the object we assign. All it does is create bindings between a target and an object. But sometimes, we may need to change one Python copy without changing the other fora mutable collection. In this Python Copy tutorial, we discuss the module copy in Python. It has the following members-


This is a simple answer which does not modify A but create another matrix which is returned:

from more_itertools import locate


def setZeroes(A):
    indexPosList = []   #  will contains all the zeroable indices
    result = []  # the result matrix
    for i in A:
        indexPosList.extend(list(locate(i, lambda a: a == 0)))
        if 0 in i:
            result.append([0]*len(i))
        else:
            for idx in indexPosList:
                i[idx] = 0
            result.append(i)
    return result


Sample test:

>>> A = [[1, 0, 1], [1, 1, 1], [1, 1, 1]]
>>> setZeroes(A)
[[0, 0, 0], [1, 0, 1], [1, 0, 1]]

How to copy an object in Python?, the object we assign. All it does is create bindings between a target and an object. But sometimes, we may need to change one Python copy… It says in the Python documentation that “Assignment statements in Python do not copy objects, they create bindings between a target and an object.” That means when we create a variable by assignment, the new variable refers to the same object as the original variable does — >>> A = [1, 2, [10, 11], 3, [20, 21]] >>> B = A >>> id (A)


Copy in Python, Assignment statements in Python do not copy objects, they create bindings between a target and an object. For collections that are mutable or contain mutable  Name binding. Python has name binding. Name binding is the association between a name and an object (value). So in Python, we bind (or attach) a name to an object. One way to bind a name to an object is to use the assignment operator (=). For example, the following code binds the name x to the object 1 (an integer whose value is 1): > > >


What are the different methods Python provides for copying an object?, MPI within a dynamic, object-oriented language. The Boost.MPI Python module can be built and installed from the libs/mpi/build directory. Just follow the  Python never implicitly copies objects. When you set dict2 = dict1, you are making them refer to the same exact dict object, so when you mutate it, all references to it keep referring to the object in its current state. If you want to copy the dict (which is rare), you have to do so explicitly with dict2 = dict(dict1)


Python Bindings - 1.72.0, So, this was all in Copy in Python. import numpy as np a = np.array([[10,10], [2,3], recursively copies object data (see examples below). from copy import copy x In Python, Assignment statements do not copy objects, they create bindings  - If a is object to be copied then copy.copy (a) returns a shallow copy of a. The copy.deepcopy (a) returns a deep copy of a. - exception copy.error: It raises for module specific errors. - Assignment statements in Python do not copy objects and they create bindings between a target and an object.