File Handling + Serialization & Deserialization-2.ipynb

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  {
   "cell_type": "markdown",
   "id": "9031e868",
   "metadata": {},
   "source": [
    "### Serialization and Deserialization\n",
    "\n",
    "- **Serialization** - process of converting python data types to JSON format\n",
    "- **Deserialization** - process of converting JSON to python data types\n",
    "\n",
    "#### What is JSON?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b928fdd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# javascript object notation\n",
    "# It is a universal datatypes which is understood by every lang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d19912fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# serialization using json module\n",
    "# list\n",
    "import json\n",
    "\n",
    "L = [1,2,3,4]\n",
    "\n",
    "with open('demo.json','w') as f:\n",
    "  json.dump(L,f)    #dump put in the file -> dumps convert it into string\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2af41892",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dict\n",
    "d = {\n",
    "    'name':'Utkarsh',\n",
    "     'age':20,\n",
    "     'gender':'male'\n",
    "}\n",
    "\n",
    "with open('demo.json','w') as f:\n",
    "  json.dump(d,f,indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d6f10718",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'Utkarsh', 'age': 20, 'gender': 'male'}\n",
      "<class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "# deserialization\n",
    "import json\n",
    "\n",
    "with open('demo.json','r') as f:\n",
    "  d = json.load(f)\n",
    "  print(d)\n",
    "  print(type(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb023fbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# serialize and deserialize tuple\n",
    "import json\n",
    "\n",
    "t = (1,2,3,4,5)\n",
    "\n",
    "with open('demo.json','w') as f:\n",
    "  json.dump(t,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6c2bb128",
   "metadata": {},
   "outputs": [],
   "source": [
    "# serialize and deserialize a nested dict\n",
    "\n",
    "d = {\n",
    "    'student':'Utkarsh',\n",
    "     'marks':[23,14,34,45,56]\n",
    "}\n",
    "\n",
    "with open('demo.json','w') as f:\n",
    "  json.dump(d,f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64eb6086",
   "metadata": {},
   "source": [
    "### Serializing and Deserializing custom objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2f3d4cbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Person:\n",
    "\n",
    "  def __init__(self,fname,lname,age,gender):\n",
    "    self.fname = fname\n",
    "    self.lname = lname\n",
    "    self.age = age\n",
    "    self.gender = gender\n",
    "\n",
    "# format to printed in\n",
    "# -> Utkarsh Upadhyay age -> 20 gender -> male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "077a6c05",
   "metadata": {},
   "outputs": [],
   "source": [
    "person = Person('Utkarsh','Upadhyay',20,'male')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9de4e7de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As a string\n",
    "import json\n",
    "\n",
    "def show_object(person):\n",
    "  if isinstance(person,Person):\n",
    "    return \"{} {} age -> {} gender -> {}\".format(person.fname,person.lname,person.age,person.gender)\n",
    "\n",
    "with open('demo.json','w') as f:\n",
    "  json.dump(person,f,default=show_object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "97998af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As a dict\n",
    "import json\n",
    "\n",
    "def show_object(person):\n",
    "  if isinstance(person,Person):\n",
    "    return {'name':person.fname + ' ' + person.lname,'age':person.age,'gender':person.gender}\n",
    "\n",
    "with open('demo.json','w') as f:\n",
    "  json.dump(person,f,default=show_object,indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5bb46473",
   "metadata": {},
   "outputs": [],
   "source": [
    "# indent arrtribute\n",
    "# As a dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2e9fa0e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'Utkarsh Upadhyay', 'age': 20, 'gender': 'male'}\n",
      "<class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "# deserializing\n",
    "import json\n",
    "\n",
    "with open('demo.json','r') as f:\n",
    "  d = json.load(f)\n",
    "  print(d)\n",
    "  print(type(d))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1ebe95d",
   "metadata": {},
   "source": [
    "### Pickling\n",
    "`Pickling` is the process whereby a Python object hierarchy is converted into a byte stream, and `unpickling` is the inverse operation, whereby a byte stream (from a binary file or bytes-like object) is converted back into an object hierarchy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6065e7a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Person:\n",
    "\n",
    "  def __init__(self,name,age):\n",
    "    self.name = name\n",
    "    self.age = age\n",
    "\n",
    "  def display_info(self):\n",
    "    print('Hi my name is',self.name,'and I am ',self.age,'years old')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "21ed8a1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = Person('Utkarsh',20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fa5e49c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle dump\n",
    "import pickle\n",
    "with open('person.pkl','wb') as f:\n",
    "  pickle.dump(p,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5540d27f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hi my name is Utkarsh and I am  20 years old\n"
     ]
    }
   ],
   "source": [
    "# pickle load\n",
    "import pickle\n",
    "with open('person.pkl','rb') as f:\n",
    "  p = pickle.load(f)\n",
    "\n",
    "p.display_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "899352d5",
   "metadata": {},
   "source": [
    "### Pickle Vs Json\n",
    "\n",
    "- Pickle lets the user to store data in binary format. JSON lets the user store data in a human-readable text format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d44a530a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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