Breadth First Search (BFS) of Graph using Python.ipynb

{
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"
    }
   },
   "cell_type": "markdown",
   "id": "1d6acd6d",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa8dfbd1",
   "metadata": {},
   "source": [
    "# Implementation of Breadth First Search (BFS) Algorithm using python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "273451b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code to create empty structure\n",
    "from collections import defaultdict \n",
    "class Graph():\n",
    "    def __init__(self):\n",
    "        self.graph=defaultdict(list)\n",
    "    def connectEdge(self,startNode,endNode):\n",
    "        self.graph[startNode].append(endNode)\n",
    "    def BFS(self,startNode):\n",
    "        queue=[]\n",
    "        visited=set()\n",
    "        queue.append(startNode)\n",
    "        visited.add(startNode)\n",
    "        print(startNode,end=\" \")\n",
    "        while queue:\n",
    "            print(queue)\n",
    "            nextNode=queue.pop(0)\n",
    "            print(nextNode)\n",
    "            for node in self.graph[nextNode]:\n",
    "                if node not in visited:\n",
    "                    queue.append(node)\n",
    "                    visited.add(node)\n",
    "                    print(node,end=\" \")\n",
    "                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3a9070c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Driver Code\n",
    "if __name__==\"__main__\":\n",
    "    root=Graph()\n",
    "    root.connectEdge(\"A\",\"C\")\n",
    "    root.connectEdge(\"A\",\"B\")\n",
    "    root.connectEdge(\"A\",\"F\")\n",
    "    root.connectEdge(\"C\",\"A\")\n",
    "    root.connectEdge(\"C\",\"D\")\n",
    "    root.connectEdge(\"C\",\"F\")\n",
    "    root.connectEdge(\"B\",\"A\")\n",
    "    root.connectEdge(\"B\",\"D\")\n",
    "    root.connectEdge(\"B\",\"E\")\n",
    "    root.connectEdge(\"B\",\"F\")\n",
    "    root.connectEdge(\"D\",\"C\")\n",
    "    root.connectEdge(\"D\",\"B\")\n",
    "    root.connectEdge(\"D\",\"E\")\n",
    "    root.connectEdge(\"E\",\"B\")\n",
    "    root.connectEdge(\"E\",\"D\")\n",
    "    root.connectEdge(\"F\",\"A\")\n",
    "    root.connectEdge(\"F\",\"C\")\n",
    "    root.connectEdge(\"F\",\"B\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "15ffac9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Vertex/Node for BFS : 2\n",
      "The BFS starting from vertex/node 2 : 2 ['2']\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "startNode=int(input(\"Starting Vertex/Node for BFS : \").upper()\n",
    "print(\"The BFS starting from vertex/node {} :\".format(startNode),end=\" \")\n",
    "root.BFS(startNode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "eaffc324",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Driver Code\n",
    "if __name__==\"__main__\":\n",
    "    root=Graph()\n",
    "    root.connectEdge(0,1)\n",
    "    root.connectEdge(0,2)\n",
    "    root.connectEdge(1,2)\n",
    "    root.connectEdge(2,0)\n",
    "    root.connectEdge(2,3)\n",
    "    root.connectEdge(3,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e1de28dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list, {0: [1, 2], 1: [2], 2: [0, 3], 3: [3], '2': []})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "root.graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0650237",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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