tutorials/GitHub_MD_rendering/Reference vs. shallow and deep copy.ipynb

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   "source": [
    "# Introduction\n",
    "<hr style = \"border:2px solid black\" ></hr>"
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   "source": [
    "\n",
    "**What?** A collection of not-so-obvious Python stuff\n",
    "\n",
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   "metadata": {},
   "source": [
    "# How to make a shallow and deep copy of an object\n",
    "<hr style = \"border:2px solid black\" ></hr>"
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      "IDs:\n",
      "list1: 140609799131584\n",
      "list2: 140609799131584\n",
      "list3: 140609799131200\n",
      "list4: 140609793850432\n",
      "list5: 140609799111104\n",
      "\n"
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   "source": [
    "from copy import deepcopy\n",
    "\n",
    "list1 = [1,2]\n",
    "list2 = list1           # reference\n",
    "list3 = list1[:]        # shallow copy\n",
    "list4 = list1.copy()    # shallow copy\n",
    "list5 = deepcopy(list1) # deep copy\n",
    "\n",
    "print('IDs:\\nlist1: {}\\nlist2: {}\\nlist3: {}\\nlist4: {}\\nlist5: {}\\n'\n",
    "      .format(id(list1), id(list2), id(list3), id(list4), id(list5)))"
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   "metadata": {},
   "source": [
    "# Shallow\n",
    "<hr style = \"border:2px solid black\" ></hr>"
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    "\n",
    "- If we use the assignment operator to assign one list to another list, we just create a new name reference to the original list.\n",
    "- If we want to create a new list object, we have to make a copy of the original list. This can be done via `a_list[:]` or `a_list.copy()`.\n",
    "\n",
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      "IDs:\n",
      "list1: 4486860424\n",
      "list2: 4486860424\n",
      "list3: 4486818632\n",
      "list4: 4486818568\n",
      "\n",
      "list1: [3, 2]\n",
      "\n",
      "list1: [3, 2]\n",
      "list2: [3, 2]\n",
      "list3: [4, 2]\n",
      "list4: [1, 4]\n"
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    "list1 = [1,2]\n",
    "list2 = list1        # reference\n",
    "list3 = list1[:]     # shallow copy\n",
    "list4 = list1.copy() # shallow copy\n",
    "\n",
    "print('IDs:\\nlist1: {}\\nlist2: {}\\nlist3: {}\\nlist4: {}\\n'\n",
    "      .format(id(list1), id(list2), id(list3), id(list4)))\n",
    "\n",
    "list2[0] = 3\n",
    "print('list1:', list1)\n",
    "\n",
    "list3[0] = 4\n",
    "list4[1] = 4\n",
    "print('\\nlist1:', list1)\n",
    "print('list2:', list2)\n",
    "print('list3:', list3)\n",
    "print('list4:', list4)"
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   "source": [
    "# Deep copy\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
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   "source": [
    "\n",
    "- If we are dealing with compound objects (e.g., lists that contain other lists, for more information) it becomes a little trickier.\n",
    "- In the case of compound objects, a shallow copy would create a new compound object, **but** it would just insert the references to the contained objects into the new compound object. In contrast, a deep copy would go \"deeper\" and create also new objects for the objects found in the original compound object. \n",
    "\n",
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      "IDs:\n",
      "list1: 4486818824\n",
      "list2: 4486886024\n",
      "list3: 4486888200\n",
      "\n",
      "list1: [[3], [2]]\n",
      "\n",
      "list1: [[3], [2]]\n",
      "list2: [[3], [2]]\n",
      "list3: [[5], [2]]\n"
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   "source": [
    "from copy import deepcopy\n",
    "\n",
    "list1 = [[1], [2]]\n",
    "list2 = list1.copy()    # shallow copy\n",
    "list3 = deepcopy(list1)  # deep copy\n",
    "\n",
    "print('IDs:\\nlist1: {}\\nlist2: {}\\nlist3: {}\\n'\n",
    "      .format(id(list1), id(list2), id(list3)))\n",
    "\n",
    "list2[0][0] = 3\n",
    "print('list1:', list1)\n",
    "\n",
    "list3[0][0] = 5\n",
    "print('\\nlist1:', list1)\n",
    "print('list2:', list2)\n",
    "print('list3:', list3)"
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   "metadata": {},
   "source": [
    "# References\n",
    "<hr style = \"border:2px solid black\" ></hr>"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "- https://nbviewer.org/github/rasbt/python_reference/blob/master/tutorials/not_so_obvious_python_stuff.ipynb?create=1\n",
    "- https://docs.python.org/2/library/copy.html\n",
    "    \n",
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   "execution_count": null,
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