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‎deep_copy.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Python: how to Copy and Deep Copy Python Lists \n",
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"(c) Joe James 2023"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Assignment is not a Copy\n",
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"listA = listB does not create a copy. Changes to one list will be reflected in the other.\n",
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"listA and listB both reference the exact same list."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[2, 44, 6, [1, 3]]\n",
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"140554034568968\n",
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"140554034568968\n"
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]
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}
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],
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"source": [
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"listA = [2, 4, 6, [1, 3]]\n",
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"listB = listA\n",
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"listB[1] = 44\n",
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"print(listA)\n",
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"print(id(listA))\n",
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"print(id(listB))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Shallow copy using the list() constructor\n",
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"Shallow copy only works for 1D lists of native data types. \n",
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"Sublists, dicts, and other objects will retain the same referece to those objects."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[2, 4, 6, [55, 3]]\n"
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]
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}
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],
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"source": [
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"listA = [2, 4, 6, [1, 3]]\n",
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"listB = list(listA)\n",
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"listB[1] = 44\n",
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"listB[3][0] = 55\n",
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"print(listA)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Other ways to make a Shallow copy\n",
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"List comprehensions, list.copy(), or copy.copy() can also be used to make *shallow* copies"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[2, 4, 6, [55, 3]]\n"
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]
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}
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],
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"source": [
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"listA = [2, 4, 6, [1, 3]]\n",
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"listB = [x for x in listA]\n",
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"listB[1] = 44\n",
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"listB[3][0] = 55\n",
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"print(listA)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[2, 4, 6, [55, 3]]\n"
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]
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}
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],
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"source": [
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"listA = [2, 4, 6, [1, 3]]\n",
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"listB = listA.copy()\n",
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"listB[1] = 44\n",
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"listB[3][0] = 55\n",
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"print(listA)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[2, 4, 6, [55, 3]]\n"
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]
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}
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],
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"source": [
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"import copy\n",
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"listA = [2, 4, 6, [1, 3]]\n",
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"listB = copy.copy(listA)\n",
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"listB[1] = 44\n",
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"listB[3][0] = 55\n",
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"print(listA)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### How to Deep Copy a Python List\n",
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"use copy.deepcopy()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[2, 4, 6, [1, 3]]\n"
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]
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}
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],
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"source": [
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"import copy\n",
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"listA = [2, 4, 6, [1, 3]]\n",
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"listB = copy.deepcopy(listA)\n",
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"listB[1] = 44\n",
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"listB[3][0] = 55\n",
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"print(listA)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Deepcopy with Objects"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"140554035637216 140554035637104\n",
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"140554035637216 140554035637216\n",
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"140554035637216 140554035637048\n"
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]
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}
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],
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"source": [
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"class Pony():\n",
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" def __init__(self, n):\n",
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" self.name = n\n",
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" \n",
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"# copy.copy on an object gives you 2 unique objects (with same attributes)\n",
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"pony1 = Pony('Pinto')\n",
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"pony2 = copy.copy(pony1)\n",
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"print(id(pony1), id(pony2))\n",
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"\n",
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"# copy.copy on a list of objects gives you 2 unique lists containing the exact same objects \n",
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"# (ie. new list is a shallow copy)\n",
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"m = [pony1, pony2]\n",
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"n = copy.copy (m)\n",
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"print(id(m[0]), id(n[0]))\n",
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"\n",
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"# use copy.deepcopy to deep copy a list of objects\n",
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"n = copy.deepcopy (m)\n",
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"print(id(m[0]), id(n[0]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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