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   "outputs": [],
   "source": [
    "%load_ext watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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     "text": [
      "Sebastian Raschka \n",
      "last updated: 2016-08-02 \n",
      "\n",
      "CPython 3.5.1\n",
      "IPython 5.0.0\n"
     ]
    }
   ],
   "source": [
    "%watermark -a 'Sebastian Raschka' -u -d -v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Queues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Queues are one of the basic, linear datastructures that have the characteristical 2 end points (here: a *front* and an *end*). Queues belong to the so-called **FIF** (*first in, first out*) data structures, which means that the first element that has been added is the the first to be removed (in contrast to the *last in, first out* LIFO principle that is implemented in stacks). So, if we `enqueue` an item to a queue, we add it to its end, and if we dequeue an item, we remove it from the front:\n",
    "\n",
    "![](images/queue-01.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basically, you can picture a queue as a line of people waiting in front of the cahsier at the supermarket. A common application of queues is the PBS/TORQUE scheduler for managing jobs on a computing cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Queue(object):\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.data = []\n",
    "    \n",
    "    def enqueue(self, item):\n",
    "        self.data.insert(0, item)\n",
    "        \n",
    "    def dequeue(self):\n",
    "        return self.data.pop()\n",
    "    \n",
    "    def show_front(self):\n",
    "        return seld.data[-1]\n",
    "\n",
    "    def show_end(self):\n",
    "        return seld.data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n",
      "['a', 1]\n",
      "[3, 'a', 1]\n",
      "1\n",
      "a\n",
      "3\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "q = Queue()\n",
    "q.enqueue(1)\n",
    "print(q.data)\n",
    "\n",
    "q.enqueue('a')\n",
    "print(q.data)\n",
    "\n",
    "q.enqueue(3)\n",
    "print(q.data)\n",
    "\n",
    "print(q.dequeue())\n",
    "print(q.dequeue())\n",
    "print(q.dequeue())\n",
    "print(q.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we used a simple python `list` object to implement a primitive example of a queue. The disadvantage of this is that one of the two operations (either enqueue or dequeue) will have a time complexity of O(n), since we have to either delete from the beginning or insert at the beginning of the `list`. A better way to implement a queue would be to use a doubly linked list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deques (not to be confused with the \"dequeue\" operation in queues) is a datastructure that is closely related to queues. Deque simply stands for \"double-ended queue,\" and as the name implies, it allows us to add and remove items at both sides of a queue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/deque-01.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Deque(object):\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.data = []\n",
    "    \n",
    "    def add_front(self, item):\n",
    "        self.data.insert(0, item)\n",
    "        \n",
    "    def remove_front(self):\n",
    "        return self.data.pop(0)\n",
    "        \n",
    "    def add_end(self, item):\n",
    "        self.data.append(item)\n",
    "        \n",
    "    def remove_end(self):\n",
    "        return self.data.pop()\n",
    "    \n",
    "    def show_front(self):\n",
    "        return seld.data[-1]\n",
    "\n",
    "    def show_end(self):\n",
    "        return seld.data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, because of the nature of Python lists, the `remove_front` and `add_front` become O(n) operations."
   ]
  }
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