{"id":146793,"date":"2025-09-08T17:59:44","date_gmt":"2025-09-08T12:29:44","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=146793"},"modified":"2025-09-08T17:59:44","modified_gmt":"2025-09-08T12:29:44","slug":"city-map-navigation-using-dsa-python","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/city-map-navigation-using-dsa-python\/","title":{"rendered":"DSA Python Project &#8211; City Map Navigation"},"content":{"rendered":"<h3>Program 1<\/h3>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\"># Project Title: City Map Navigation using Graphs\r\n\r\n# Objective:\r\n# To build a simple navigation system that finds the shortest path between a source city \r\n# and all other cities using Dijkstra's Algorithm \u2014 one of the most efficient ways to compute \r\n# shortest paths in a weighted graph.\r\n\r\n# Data Structures Used:\r\n\r\n# Graph (Adjacency Matrix):\r\n# Stores the map as a 2D array where each cell adjMatrix[i][j] \r\n# represents the distance between city i and city j.\r\n# If there is no direct road, the distance is set to a high value (INF).\r\n# Arrays:\r\n\r\n# dist[]: To hold the minimum distances from the source city.\r\n# visited[]: To keep track of cities already included in the shortest path tree.\r\n# prev[]: To reconstruct the path (stores the previous city for each destination).\r\n\r\n# Project Work Flow:\r\n\r\n# 1. City and Road Input:\r\n# The user first inputs how many cities are in the map.\r\n# For each city, a name is provided (e.g., \"Indore\", \"Pune\").\r\n# The user then enters how many roads exist and the distance between city pairs using their indices.\r\n\r\n# Option 1: Show Adjacency Matrix\r\n\r\n# Displays a table of all city-to-city distances.\r\n# Useful for visualizing the city graph.\r\n\r\n# Option 2: Find Shortest Paths\r\n\r\n# User selects a source city.\r\n# The program uses Dijkstra\u2019s algorithm to calculate the shortest distances from the \r\n# source to all other cities.\r\n# It also prints the actual path taken from the source to each city.\r\n\r\n# Option 3: Exit\r\n\r\n\r\n\r\nimport sys\r\n\r\nINF = float('inf')\r\n\r\nclass Graph:\r\n    def __init__(self, vertices):  # 4\r\n        self.vertices = vertices\r\n        self.adj_matrix = [[INF] * vertices for _ in range(vertices)]\r\n        self.city_names = [\"\"] * vertices\r\n\r\n    def add_edge(self, src, dest, weight):   # 0 1 300\r\n        self.adj_matrix[src][dest] = weight\r\n        self.adj_matrix[dest][src] = weight  # Undirected\r\n\r\n    def set_city_name(self, index, name):\r\n        self.city_names[index] = name\r\n\r\n    def display_matrix(self):\r\n        print(\"\\nAdjacency Matrix:\")\r\n        print(\"       \", end=\"\")\r\n        for name in self.city_names:\r\n            print(f\"{name:&gt;10}\", end=\"\")\r\n        print()\r\n        for i in range(self.vertices):             # row\r\n            print(f\"{self.city_names[i]:&gt;10}\", end=\"\")\r\n            for j in range(self.vertices):       # col\r\n                if self.adj_matrix[i][j] == INF:\r\n                    print(f\"{'\u221e':&gt;10}\", end=\"\")\r\n                else:\r\n                    print(f\"{self.adj_matrix[i][j]:&gt;10}\", end=\"\")\r\n            print()\r\n\r\n    def dijkstra(self, src):  # 0\r\n        dist = [INF] * self.vertices\r\n        visited = [False] * self.vertices\r\n        prev = [-1] * self.vertices\r\n        dist[src] = 0\r\n\r\n        for _ in range(self.vertices - 1):\r\n            u = self.min_distance(dist, visited)\r\n            visited[u] = True\r\n\r\n            for v in range(self.vertices):\r\n                if not visited[v] and self.adj_matrix[u][v] != INF and dist[u] + self.adj_matrix[u][v] &lt; dist[v]:\r\n                    dist[v] = dist[u] + self.adj_matrix[u][v]\r\n                    prev[v] = u\r\n\r\n        print(f\"\\nShortest paths from {self.city_names[src]}:\")\r\n        for i in range(self.vertices):\r\n            if i != src:\r\n                print(f\"To {self.city_names[i]} - Distance: {dist[i] if dist[i] != INF else '\u221e'} - Path: \", end=\"\")\r\n                self.print_path(prev, i)\r\n                print(self.city_names[i])\r\n\r\n    def min_distance(self, dist, visited):\r\n        min_val = INF\r\n        min_index = -1\r\n        for i in range(self.vertices):\r\n            if not visited[i] and dist[i] &lt; min_val:\r\n                min_val = dist[i]\r\n                min_index = i\r\n        return min_index\r\n\r\n    def print_path(self, prev, j):\r\n        if prev[j] == -1:\r\n            return\r\n        self.print_path(prev, prev[j])\r\n        print(self.city_names[prev[j]] + \" -&gt; \", end=\"\")\r\n\r\n# -------- Main Program --------\r\n\r\ndef main():\r\n    n = int(input(\"Enter number of cities: \"))   #n= 4\r\n    g = Graph(n)   # 4\r\n\r\n    for i in range(n):\r\n        name = input(f\"Enter name of city {i}: \")\r\n        g.set_city_name(i, name)\r\n\r\n    e = int(input(\"Enter number of roads: \"))\r\n    for i in range(e):\r\n        print(f\"Enter road {i+1} (source_index destination_index distance): \", end=\"\")\r\n        src, dest, dist = map(int, input().split())\r\n        g.add_edge(src, dest, dist)  # 0 1 200\r\n\r\n    while True:\r\n        print(\"\\n---------- City Map Navigation Menu -------------\")\r\n        print(\"1. Show Adjacency Matrix\")\r\n        print(\"2. Find Shortest Paths\")\r\n        print(\"3. Exit\")\r\n        print(\"-------------------------------------------------\")\r\n        choice = int(input(\"Enter your choice: \"))\r\n\r\n        if choice == 1:\r\n            g.display_matrix()\r\n        elif choice == 2:\r\n            src = int(input(\"Enter source city index: \"))  # 0\r\n            g.dijkstra(src)  # 0\r\n        elif choice == 3:\r\n            print(\"Exiting...\")\r\n            break\r\n        else:\r\n            print(\"Invalid choice. Try again.\")\r\n\r\nif __name__ == \"__main__\":\r\n    main()<\/pre>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Program 1 # Project Title: City Map Navigation using Graphs # Objective: # To build a simple navigation system that finds the shortest path between a source city # and all other cities using&#46;&#46;&#46;<\/p>\n","protected":false},"author":581,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[32847],"tags":[34953,34956,35232,32853,35233,32922,32923,35234,34829],"class_list":["post-146793","post","type-post","status-publish","format-standard","hentry","category-dsa-python-tutorials","tag-city-map-navigation","tag-city-map-navigation-project","tag-city-map-navigation-using-dsa-python","tag-dsa-python","tag-dsa-python-city-map-navigation-project","tag-dsa-python-practical","tag-dsa-python-program","tag-dsa-python-program-on-city-map-navigation","tag-dsa-python-project"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>DSA Python Project - City Map Navigation - DataFlair<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/city-map-navigation-using-dsa-python\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"DSA Python Project - City Map Navigation - DataFlair\" \/>\n<meta property=\"og:description\" content=\"Program 1 # Project Title: City Map Navigation using Graphs # Objective: # To build a simple navigation system that finds the shortest path between a source city # and all other cities using&#046;&#046;&#046;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/data-flair.training\/blogs\/city-map-navigation-using-dsa-python\/\" \/>\n<meta property=\"og:site_name\" content=\"DataFlair\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataFlairWS\/\" \/>\n<meta property=\"article:published_time\" content=\"2025-09-08T12:29:44+00:00\" \/>\n<meta name=\"author\" content=\"DataFlair Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:site\" content=\"@DataFlairWS\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"DataFlair Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"DSA Python Project - City Map Navigation - DataFlair","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/data-flair.training\/blogs\/city-map-navigation-using-dsa-python\/","og_locale":"en_US","og_type":"article","og_title":"DSA Python Project - City Map Navigation - DataFlair","og_description":"Program 1 # Project Title: City Map Navigation using Graphs # Objective: # To build a simple navigation system that finds the shortest path between a source city # and all other cities using&#46;&#46;&#46;","og_url":"https:\/\/data-flair.training\/blogs\/city-map-navigation-using-dsa-python\/","og_site_name":"DataFlair","article_publisher":"https:\/\/www.facebook.com\/DataFlairWS\/","article_published_time":"2025-09-08T12:29:44+00:00","author":"DataFlair Team","twitter_card":"summary_large_image","twitter_creator":"@DataFlairWS","twitter_site":"@DataFlairWS","twitter_misc":{"Written by":"DataFlair Team","Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/data-flair.training\/blogs\/city-map-navigation-using-dsa-python\/#article","isPartOf":{"@id":"https:\/\/data-flair.training\/blogs\/city-map-navigation-using-dsa-python\/"},"author":{"name":"DataFlair Team","@id":"https:\/\/data-flair.training\/blogs\/#\/schema\/person\/c187795dc82ab948373cca526df7c445"},"headline":"DSA Python Project &#8211; 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