3_adv_features_3.ipynb

{
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   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "<font size=6> <b> Advanced Python : week #4</b> </font>\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "   Advanced Python features <br>\n",
    "    <ol>\n",
    "        <li> Lambda </b>\n",
    "        <li> map, filter, reduce, functools.partial 등 </li>\n",
    "        <li> @Property Decorator </li>\n",
    "        <li> del, garbage collection </li>\n",
    "    </ol>\n",
    "</div>\n",
    "\n",
    "<p style=\"text-align:right;\"> sumyeon@gmail.com </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lambda : anonymous small function\n",
    "> one-liner <b>(single expression)</b> function but can takes any number of arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "func_with_name = lambda x,y : x+y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "func_with_name(10,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Map, Filter, Reduce, functools.reduce, functools.partial\n",
    "- function tools which can be applied to iterables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import functools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### map\n",
    "- apply first argument (method) to the second argument (iterable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plus_one(a):\n",
    "    return a+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 4, 6, 8, 10]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[val for val in map(plus_one, range(1,10,2))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### filter\n",
    "- based on the result of applying first argument to the second arguemnt, show the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 5, 7, 9]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[val for val in filter(lambda x : x % 2, range(1,10,2))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### reduce\n",
    "- apply the first argument method to the first two values in the second argument  <br>\n",
    " , and then apply the method to the return value and third value, and so on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "functools.reduce(lambda x,y: x+y, range(1,11))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### partial\n",
    "- apply the arguments from the second to the default value of arguments of the first argument method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plus_x(value, plus_val):\n",
    "    return value+plus_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plus_x(10, 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plus_11 = functools.partial(plus_x, plus_val=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plus_11(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1010, 10)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basetwo = functools.partial(int, base=2)\n",
    "int('1010'), basetwo('1010')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# all or any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [True, True, False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "any(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2021-09-17T23:51:21.271754Z"
    }
   },
   "source": [
    "#  @Property Decorator\n",
    "- python built-in decorator : it can make the use of getter/setter much easier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's implement a class with percentage grade value (between 0 and 100) and a method returning relevant lettergrade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Final Correct Answer (pythoniac way)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class FinalCollegeGrade:\n",
    "    letter_grade = ['A','B','C','D','E/F']\n",
    "    percentscale_min = [90, 80, 70, 65, 0]\n",
    "    \n",
    "    def __init__(self, percent=0):\n",
    "        self.percent = percent\n",
    "        \n",
    "    def to_lettergrade(self):\n",
    "        return self.letter_grade[sum( map( lambda x : self.percent < x, self.percentscale_min))]\n",
    "    \n",
    "    @property\n",
    "    def percent(self):\n",
    "        return self._percent\n",
    "    \n",
    "    @percent.setter\n",
    "    def percent(self, value):\n",
    "        if 0 <= value <= 100:\n",
    "            self._percent = value\n",
    "        else:\n",
    "            raise ValueError(f\"{value} should be between 0 and 100\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "final = FinalCollegeGrade(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.to_lettergrade()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.percent = 70\n",
    "final.to_lettergrade()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "110 should be between 0 and 100",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-360515036da4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfinal\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpercent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m110\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-13-40302ec992e1>\u001b[0m in \u001b[0;36mpercent\u001b[1;34m(self, value)\u001b[0m\n\u001b[0;32m     18\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_percent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{value} should be between 0 and 100\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: 110 should be between 0 and 100"
     ]
    }
   ],
   "source": [
    "final.percent = 110"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Class definition without filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class CollegeGrade1:\n",
    "    letter_grade = ['A','B','C','D','E/F']\n",
    "    percentscale_min = [90, 80, 70, 65, 0]\n",
    "    def __init__(self, percent=0):\n",
    "        self.percent = percent\n",
    "        \n",
    "    def to_lettergrade(self):\n",
    "        return self.letter_grade[sum( map( lambda x : self.percent < x, self.percentscale_min))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'CollegeGrade1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-18-8bacf332a20e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0melementary\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCollegeGrade1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m80\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0melementary\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_lettergrade\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'CollegeGrade1' is not defined"
     ]
    }
   ],
   "source": [
    "elementary = CollegeGrade1(80)\n",
    "elementary.to_lettergrade()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'CollegeGrade1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-f3d49b6907df>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0melementary\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCollegeGrade1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0melementary\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_lettergrade\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'CollegeGrade1' is not defined"
     ]
    }
   ],
   "source": [
    "elementary = CollegeGrade1(200)\n",
    "elementary.to_lettergrade()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'elementary' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-7d0069ca678b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0melementary\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpercent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0melementary\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_lettergrade\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'elementary' is not defined"
     ]
    }
   ],
   "source": [
    "elementary.percent = -10\n",
    "elementary.to_lettergrade()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, let's check the percent value to be '0 <= percent <= 100' \n",
    " - filter the input before initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class CollegeGrade2:\n",
    "    letter_grade = ['A','B','C','D','E/F']\n",
    "    percentscale_min = [90, 80, 70, 65, 0]\n",
    "    def __init__(self, percent=0):\n",
    "        if 0 <= percent <= 100:\n",
    "            self.percent = percent\n",
    "        else:\n",
    "            raise ValueError(f\"{percent} should be between 0 and 100\")\n",
    "        \n",
    "    def to_lettergrade(self):\n",
    "        return self.letter_grade[sum( map( lambda x : self.percent < x, self.percentscale_min))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'B'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "middle = CollegeGrade2(88)\n",
    "middle.to_lettergrade()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "200 should be between 0 and 100",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-b2dbb7823efb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmiddle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCollegeGrade2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mmiddle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_lettergrade\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-21-039c7f1274a1>\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, percent)\u001b[0m\n\u001b[0;32m      6\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpercent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpercent\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{percent} should be between 0 and 100\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mto_lettergrade\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: 200 should be between 0 and 100"
     ]
    }
   ],
   "source": [
    "middle = CollegeGrade2(200)\n",
    "middle.to_lettergrade()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "middle.percent = 200\n",
    "middle.to_lettergrade()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### let's check the percent value to be '0 <= percent <= 100' \n",
    " - we should not set the percent variable directly\n",
    " - implement getter/setter method for the percent variable\n",
    " - make the local variable to be protected (NOTE: just convention)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class CollegeGrade3:\n",
    "    letter_grade = ['A','B','C','D','E/F']\n",
    "    percentscale_min = [90, 80, 70, 65, 0]\n",
    "    \n",
    "    def __init__(self, percent=0):\n",
    "        self.set_percent(percent)\n",
    "        \n",
    "    def to_lettergrade(self):\n",
    "        return self.letter_grade[sum( map( lambda x : self._percent < x, self.percentscale_min))]\n",
    "    \n",
    "    def get_percent(self):\n",
    "        return self._percent\n",
    "    \n",
    "    def set_percent(self, value):\n",
    "        if 0 <= value <= 100:\n",
    "            self._percent = value\n",
    "        else:\n",
    "            raise ValueError(f\"{value} should be between 0 and 100\")\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> we have to set/get the variable via methods and still local variable '_percent' is accessible from anywhere"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('D', 67)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high = CollegeGrade3(67)\n",
    "high.to_lettergrade(), high.get_percent()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "200 should be between 0 and 100",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-57e83d0f0265>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mhigh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_percent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-25-6bcaa082f370>\u001b[0m in \u001b[0;36mset_percent\u001b[1;34m(self, value)\u001b[0m\n\u001b[0;32m     16\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_percent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{value} should be between 0 and 100\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: 200 should be between 0 and 100"
     ]
    }
   ],
   "source": [
    "high.set_percent(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('D', 67)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high.percent = 200\n",
    "high.to_lettergrade(), high.get_percent()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('A', 200)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high._percent = 200\n",
    "high.to_lettergrade(), high.get_percent()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### let's make the getter/setter to be like property"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class CollegeGrade4:\n",
    "    letter_grade = ['A','B','C','D','E/F']\n",
    "    percentscale_min = [90, 80, 70, 65, 0]\n",
    "    \n",
    "    def __init__(self, percent=0):\n",
    "        self.set_percent(percent)\n",
    "        \n",
    "    def to_lettergrade(self):\n",
    "        return self.letter_grade[sum( map( lambda x : self.percent < x, self.percentscale_min))]\n",
    "    \n",
    "    def get_percent(self):\n",
    "        return self._percent\n",
    "    \n",
    "    def set_percent(self, value):\n",
    "        if 0 <= value <= 100:\n",
    "            self._percent = value\n",
    "        else:\n",
    "            raise ValueError(f\"{value} should be between 0 and 100\")\n",
    "            \n",
    "    percent = property(get_percent, set_percent)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> still, we can change the value of _percent, but 'percent' property acts like a variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(95, 'A')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ = CollegeGrade4(95)\n",
    "univ.percent, univ.to_lettergrade()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85, 'B')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "univ.percent = 85\n",
    "univ.percent, univ.to_lettergrade()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "200 should be between 0 and 100",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-33-04e5425bfb9c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0muniv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpercent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m200\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-30-9452f9c76d19>\u001b[0m in \u001b[0;36mset_percent\u001b[1;34m(self, value)\u001b[0m\n\u001b[0;32m     16\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_percent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{value} should be between 0 and 100\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m     \u001b[0mpercent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mproperty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mget_percent\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mset_percent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: 200 should be between 0 and 100"
     ]
    }
   ],
   "source": [
    "univ.percent = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 'A')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# still the local variable is accessible from everywher\n",
    "univ._percent = 200\n",
    "univ.percent, univ.to_lettergrade()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Memory management"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### garbage collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gc\n",
    "gc.isenabled()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.get_debug()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "a = 'hello'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "del a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gc.set_debug(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "a = 'hello'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "del a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "gc: collecting generation 2...\n",
      "gc: objects in each generation: 211 0 84196\n",
      "gc: objects in permanent generation: 0\n",
      "gc: done, 0 unreachable, 0 uncollectable, 0.0310s elapsed\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect(generation=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gc.set_debug(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Memory Address & Reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2004494745872"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "punk = 2077\n",
    "id(punk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2077\n"
     ]
    }
   ],
   "source": [
    "import _ctypes\n",
    "print(_ctypes.PyObj_FromPtr(2004494745872))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2004494745904, 2004494745904)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "punk = 2077\n",
    "yang = punk\n",
    "\n",
    "id(punk), id(yang)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2077\n"
     ]
    }
   ],
   "source": [
    "print(_ctypes.PyObj_FromPtr(2004494745904))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "del punk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2077\n"
     ]
    }
   ],
   "source": [
    "print(_ctypes.PyObj_FromPtr(2004494745904))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "del yang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8192\n"
     ]
    }
   ],
   "source": [
    "print(_ctypes.PyObj_FromPtr(2004494745904))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interning Mechanism\n",
    "> for the performance, [-5,256] and small-size string are created once and re-used many times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=256\n",
    "b=256\n",
    "a==b, a is b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c=257\n",
    "d=257\n",
    "c==d, c is d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "> integer range for caching \n",
    "[-5,256] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p='hello'\n",
    "q='hello'\n",
    "p==q, p is q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, False)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r='In order to save time and memory costs, Python always pre-loads all the small integers'\n",
    "s='In order to save time and memory costs, Python always pre-loads all the small integers'\n",
    "r==s, r is s"
   ]
  }
 ],
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