collection_examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Collections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Following are the container datatypes\n",
    "\n",
    "* namedtupple\n",
    "* deque\n",
    "* Counter\n",
    "* OrderedDict\n",
    "* DefaultDict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### namedtupple\n",
    "\n",
    "* <b>Returns a new tuple subclass named typename. \n",
    "* The new subclass is used to create tuple-like objects that have fields accessible by attribute lookup as well as being indexable and iterable.\n",
    "* Instances of the subclass also have a helpful docstring (with typename and field_names) and a helpful __repr__() method which lists the tuple contents in a name=value format.</b>\n",
    "\n",
    "#### namedtuple(typename, field_names, verbose=False, rename=False)\n",
    "\n",
    "|Method|Description|\n",
    "|------|-----------|\n",
    "|<b>_asdict()</b>|Return a new OrderedDict which maps field names to their corresponding values.|\n",
    "|<b>_make(iterable)</b>|Class method that makes a new instance from an existing sequence or iterable.|\n",
    "|<b>_fields()</b>|Tuple of strings listing the field names.Useful for introspection and for creating new named tuple types from existing named tuples.|\n",
    "|<b>_replace(kwargs)</b>|Return a new instance of the named tuple replacing specified fields with new values|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import namedtuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Rectangle = namedtuple('Rectangle',['width','height'],verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'type'>\n"
     ]
    }
   ],
   "source": [
    "print type(Rectangle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class '__main__.Rectangle'>\n"
     ]
    }
   ],
   "source": [
    "r1 = Rectangle(10,20)\n",
    "print type(r1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rectangle(width=10, height=20)\n",
      "OrderedDict([('width', 10), ('height', 20)])\n",
      "[Rectangle(width=10, height=20), Rectangle(width=1, height=2), Rectangle(width=5, height=8)]\n",
      "('width', 'height')\n",
      "Rectangle(width=12, height=20)\n",
      "20\n"
     ]
    }
   ],
   "source": [
    "print r1\n",
    "print r1._asdict()\n",
    "rect = [(10,20),(1,2),(5,8)]\n",
    "print map(Rectangle._make,rect)\n",
    "print r1._fields\n",
    "print r1._replace(width=12)\n",
    "print getattr(r1,'height')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "countryRecord(country_name='Afghanistan', country_code='93', country_iso_code='AF / AFG')\n",
      "Afghanistan AF / AFG\n",
      "countryRecord(country_name='Albania', country_code='355', country_iso_code='AL / ALB')\n",
      "Albania AL / ALB\n",
      "countryRecord(country_name='Algeria', country_code='213', country_iso_code='DZ / DZA')\n",
      "Algeria DZ / DZA\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "country_record = namedtuple('countryRecord',['country_name','country_code','country_iso_code'],verbose=False)\n",
    "country_tupple = map(country_record._make,csv.reader(open(\"country_codes.csv\",\"rb\")))\n",
    "for c in country_tupple[0:3]:\n",
    "    print c\n",
    "    print c.country_name,c.country_iso_code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### deque\n",
    "\n",
    "* <b>Returns a new deque object initialized left-to-right (using append()) with data from iterable.</b>\n",
    "* <b>If iterable is not specified, the new deque is empty.</b>\n",
    "* <b>Deques are a generalization of stacks and queues (the name is pronounced “deck” and is short for “double-ended queue”).</b>\n",
    "* <b>Deques support thread-safe, memory efficient appends and pops from either side of the deque</b>\n",
    "* <b>Deques may grow to an arbitrary length</b>\n",
    "\n",
    "|Method                |Description                            |\n",
    "|----------------------|---------------------------------------|\n",
    "|<b>append(x)</b>      |Add x to the right side of the deque.|\n",
    "|<b>appendLeft(x)</b>  |Add x to the left side of the deque.|\n",
    "|<b>clear()</b>        |Remove all elements from the deque leaving it with length 0.|\n",
    "|<b>count(x)</b>       |Count the number of deque elements equal to x.|\n",
    "|<b>extend(iterable)</b>|Extend the right side of the deque by appending elements from the iterable argument|\n",
    "|<b>extendleft(iterable)</b>|Extend the left side of the deque by appending elements from iterable. Note, the series of left appends results in reversing the order of elements in the iterable argument.|\n",
    "|<b>pop()</b>|Remove and return an element from the right side of the deque. If no elements are present, raises an IndexError.|\n",
    "|<b>popleft()</b>|Remove and return an element from the left side of the deque. If no elements are present, raises an IndexError.|\n",
    "|<b>remove(value)</b>|Removed the first occurrence of value. If not found, raises a ValueError.|\n",
    "|<b>reverse()</b>|Reverse the elements of the deque in-place and then return None.|\n",
    "|<b>rotate(n)</b>|Rotate the deque n steps to the right. If n is negative, rotate to the left. Rotating one step to the right is equivalent to: d.appendleft(d.pop()).|\n",
    "|<b>maxlen</b>|Maximum size of a deque or None if unbounded.|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Indexed access is O(1) at both ends but slows to O(n) in the middle. For fast random access, use lists instead.</b>\n",
    "\n",
    "<b>Adding to or removing from the beginning of a list is O(n), but fetching elements from the middle is O(1). For a deque, the reverse is true.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# random access - go for list\n",
    "# accessing at the ends - go for deque"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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PF5/Pi8frxa8oKH5Fny2iacEI4AKBQDDckHuT5sq6xdwgGzCb9CE2i9mC1WrB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\nNTkwNiwgdW5pdHM9MRbC6PkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython import display\n",
    "display.Image(\"basicdeque.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from collections import deque"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'collections.deque'>\n"
     ]
    }
   ],
   "source": [
    "numberList = range(1,100000)\n",
    "myque = deque(numberList)\n",
    "print type(myque)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_element(deque_object):\n",
    "    a = deque_object.pop()\n",
    "    deque_object.append(a)\n",
    "    return a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99999\n",
      "99999\n"
     ]
    }
   ],
   "source": [
    "print get_element(myque)\n",
    "print len(myque)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100002\n",
      "100002\n"
     ]
    }
   ],
   "source": [
    "newList = [100000,100001,100002]\n",
    "myque.extend(newList)\n",
    "print get_element(myque)\n",
    "print len(myque)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100002\n"
     ]
    }
   ],
   "source": [
    "try :\n",
    "    value = 100001\n",
    "    myque.remove(value)\n",
    "except ValueError:\n",
    "    print \"Value \" + str(value) + \" not in myque\"\n",
    "print get_element(myque)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rectangle(width=50, height=50)\n"
     ]
    }
   ],
   "source": [
    "rect = [(10,10),(20,20),(30,30),(40,40),(50,50)]\n",
    "my_rect_deque = deque(map(Rectangle._make,rect))\n",
    "print get_element(my_rect_deque)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rectangle(width=50, height=50)\n",
      "Rectangle(width=40, height=40)\n",
      "Rectangle(width=30, height=30)\n",
      "Rectangle(width=20, height=20)\n",
      "Rectangle(width=10, height=10)\n"
     ]
    }
   ],
   "source": [
    "for a in range(len(my_rect_deque)):\n",
    "    x = my_rect_deque.pop()\n",
    "    print x\n",
    "    my_rect_deque.appendleft(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "my_rect_deque.rotate(-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import pandas as pd\n",
    "#import numpy as np\n",
    "#df = pd.read_html('https://countrycode.org/')[1]\n",
    "#df.to_csv('country_codes.csv',index=False,header=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counter\n",
    "\n",
    "* <b>A Counter is a dict subclass for counting hashable objects. \n",
    "* It is an unordered collection where elements are stored as dictionary keys and their counts are stored as dictionary values. \n",
    "* Counts are allowed to be any integer value including zero or negative counts. \n",
    "* The Counter class is similar to bags or multisets in other languages.\n",
    "* Elements are counted from an iterable or initialized from another mapping (or counter):</b>\n",
    "\n",
    "#### Counter objects support three methods beyond those available for all dictionaries:\n",
    "\n",
    "|Method|Description|\n",
    "|------|-----------|\n",
    "|<b>elements()</b>|Return an iterator over elements repeating each as many times as its count.Elements are returned in arbitrary order.If an element’s count is less than one, elements() will ignore it.|\n",
    "|<b>most_common([n])></b>|Return a list of the n most common elements and their counts from the most common to the least.If n is omitted or None, most_common() returns all elements in the counter.Elements with equal counts are ordered arbitrarily:|\n",
    "|<b>subtract([iterable-or-mapping])</b>|Elements are subtracted from an iterable or from another mapping (or counter).Like dict.update() but subtracts counts instead of replacing them.Both inputs and outputs may be zero or negative.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "votes = ['bjp','congress','aap','bjp','bjp','bjp','congress','aap','bjp','bjp','jdal','bjp','congress','aap']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "votes_counter = Counter(votes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'bjp': 7, 'aap': 3, 'congress': 3, 'jdal': 1})\n"
     ]
    }
   ],
   "source": [
    "print votes_counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['jdal', 'aap', 'aap', 'aap', 'congress', 'congress', 'congress', 'bjp', 'bjp', 'bjp', 'bjp', 'bjp', 'bjp', 'bjp']\n"
     ]
    }
   ],
   "source": [
    "print list(votes_counter.elements())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('bjp', 7), ('aap', 3), ('congress', 3)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "votes_counter.most_common(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "invalid_votes = ['bjp','aap','congress','newparty']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'bjp': 6, 'aap': 2, 'congress': 2, 'jdal': 1, 'newparty': -1})\n"
     ]
    }
   ],
   "source": [
    "total_votes = Counter(votes)\n",
    "total_votes.subtract(invalid_votes)\n",
    "print total_votes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'bjp': 7, 'aap': 3, 'congress': 2, 'jdal': 1, 'others': 1, 'newparty': -1})\n"
     ]
    }
   ],
   "source": [
    "total_votes.update(['aap','bjp','others'])\n",
    "print total_votes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OrderedDict\n",
    "\n",
    "* <b>Return an instance of a dict subclass, supporting the usual dict methods.\n",
    "* An OrderedDict is a dict that remembers the order that keys were first inserted.\n",
    "* If a new entry overwrites an existing entry, the original insertion position is left unchanged.\n",
    "* Deleting an entry and reinserting it will move it to the end.</b>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import OrderedDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_ordered_dict = OrderedDict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_ordered_dict.update({'Root':47})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_ordered_dict.update({'Parent1':40})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_ordered_dict.update({'Parent2':38})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('Root', 47), ('Parent1', 40), ('Parent2', 38)])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_ordered_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xx = OrderedDict([('a',10),('b',20),('c',40)])\n",
    "Person = namedtuple('Person',['name','age'],verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "persons = map(Person._make,[('jonh',25),('mary',16),('david',20),('karmen',5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "persons_od = OrderedDict(sorted(persons,key=lambda x:x.age))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('karmen', 5), ('mary', 16), ('david', 20), ('jonh', 25), ('Tiger', 60)])\n"
     ]
    }
   ],
   "source": [
    "persons_od.update({'Tiger':60})\n",
    "print persons_od"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('mary', 16), ('david', 20), ('jonh', 25), ('Tiger', 60)])\n"
     ]
    }
   ],
   "source": [
    "persons_od.pop('karmen')\n",
    "print persons_od"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Tiger', 60)\n"
     ]
    }
   ],
   "source": [
    "print persons_od.popitem()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('mary', 16)\n"
     ]
    }
   ],
   "source": [
    "print persons_od.popitem(last=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jonh\n",
      "david\n"
     ]
    }
   ],
   "source": [
    "for x in reversed(persons_od):\n",
    "    print x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### defaultdict\n",
    "\n",
    "* <b>Returns a new dictionary-like object. \n",
    "* defaultdict is a subclass of the built-in dict class. \n",
    "* It overrides one method and adds one writable instance variable. \n",
    "* The remaining functionality is the same as for the dict class.\n",
    "* The first argument provides the initial value for the default_factory attribute; it defaults to None.\n",
    "* All remaining arguments are treated the same as if they were passed to the dict constructor, including keyword arguments.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<type 'list'>, {'aap': ['Kejariwal', 'Manish'], 'congress': ['Sonia', 'Rahul'], 'bjp': ['Modi', 'Rajnath']})\n"
     ]
    }
   ],
   "source": [
    "my_list = [('bjp','Modi'),('aap','Kejariwal'),('bjp','Rajnath'),('congress','Sonia'),('congress','Rahul'),('aap','Manish')]\n",
    "my_def_dict = defaultdict(list)\n",
    "for k,v in my_list:\n",
    "    my_def_dict[k].append(v)\n",
    "print my_def_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Modi', 'Rajnath']\n"
     ]
    }
   ],
   "source": [
    "bjp_party_people = my_def_dict.get('bjp')\n",
    "print bjp_party_people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ramayan_characters = defaultdict(Person)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<class '__main__.Person'>, {'Sita': Person(name='Sita', age=28), 'Ram': Person(name='Ram', age=35), 'Lakshman': Person(name='Lakshman', age=30)})\n"
     ]
    }
   ],
   "source": [
    "ramayan_characters['Ram'] = Person('Ram',35)\n",
    "ramayan_characters['Lakshman'] = Person('Lakshman',30)\n",
    "ramayan_characters['Sita'] = Person('Sita',28)\n",
    "print ramayan_characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person(name='Lakshman', age=29)\n"
     ]
    }
   ],
   "source": [
    "ramayan_characters['Lakshman'] = ramayan_characters['Lakshman']._replace(age=29)\n",
    "print ramayan_characters['Lakshman']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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