diff --git a/DIRECTORY.md b/DIRECTORY.md
index 80859356c..b57cb2eb1 100644
--- a/DIRECTORY.md
+++ b/DIRECTORY.md
@@ -287,6 +287,7 @@
   * [Minimum Spanning Tree Kruskal](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_kruskal.py)
   * [Minimum Spanning Tree Kruskal2](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_kruskal2.py)
   * [Minimum Spanning Tree Prims](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_prims.py)
+  * [Minimum Spanning Tree Prims2](https://github.com/TheAlgorithms/Python/blob/master/graphs/minimum_spanning_tree_prims2.py)
   * [Multi Heuristic Astar](https://github.com/TheAlgorithms/Python/blob/master/graphs/multi_heuristic_astar.py)
   * [Page Rank](https://github.com/TheAlgorithms/Python/blob/master/graphs/page_rank.py)
   * [Prim](https://github.com/TheAlgorithms/Python/blob/master/graphs/prim.py)
diff --git a/graphs/minimum_spanning_tree_prims2.py b/graphs/minimum_spanning_tree_prims2.py
new file mode 100644
index 000000000..10ed736c9
--- /dev/null
+++ b/graphs/minimum_spanning_tree_prims2.py
@@ -0,0 +1,271 @@
+"""
+Prim's (also known as Jarník's) algorithm is a greedy algorithm that finds a minimum
+spanning tree for a weighted undirected graph. This means it finds a subset of the
+edges that forms a tree that includes every vertex, where the total weight of all the
+edges in the tree is minimized. The algorithm operates by building this tree one vertex
+at a time, from an arbitrary starting vertex, at each step adding the cheapest possible
+connection from the tree to another vertex.
+"""
+
+from sys import maxsize
+from typing import Dict, Optional, Tuple, Union
+
+
+def get_parent_position(position: int) -> int:
+    """
+    heap helper function get the position of the parent of the current node
+
+    >>> get_parent_position(1)
+    0
+    >>> get_parent_position(2)
+    0
+    """
+    return (position - 1) // 2
+
+
+def get_child_left_position(position: int) -> int:
+    """
+    heap helper function get the position of the left child of the current node
+
+    >>> get_child_left_position(0)
+    1
+    """
+    return (2 * position) + 1
+
+
+def get_child_right_position(position: int) -> int:
+    """
+    heap helper function get the position of the right child of the current node
+
+    >>> get_child_right_position(0)
+    2
+    """
+    return (2 * position) + 2
+
+
+class MinPriorityQueue:
+    """
+    Minimum Priority Queue Class
+
+    Functions:
+    is_empty: function to check if the priority queue is empty
+    push: function to add an element with given priority to the queue
+    extract_min: function to remove and return the element with lowest weight (highest
+                 priority)
+    update_key: function to update the weight of the given key
+    _bubble_up: helper function to place a node at the proper position (upward
+                movement)
+    _bubble_down: helper function to place a node at the proper position (downward
+                movement)
+    _swap_nodes: helper function to swap the nodes at the given positions
+
+    >>> queue = MinPriorityQueue()
+
+    >>> queue.push(1, 1000)
+    >>> queue.push(2, 100)
+    >>> queue.push(3, 4000)
+    >>> queue.push(4, 3000)
+
+    >>> print(queue.extract_min())
+    2
+
+    >>> queue.update_key(4, 50)
+
+    >>> print(queue.extract_min())
+    4
+    >>> print(queue.extract_min())
+    1
+    >>> print(queue.extract_min())
+    3
+    """
+
+    def __init__(self) -> None:
+        self.heap = []
+        self.position_map = {}
+        self.elements = 0
+
+    def __len__(self) -> int:
+        return self.elements
+
+    def __repr__(self) -> str:
+        return str(self.heap)
+
+    def is_empty(self) -> bool:
+        # Check if the priority queue is empty
+        return self.elements == 0
+
+    def push(self, elem: Union[int, str], weight: int) -> None:
+        # Add an element with given priority to the queue
+        self.heap.append((elem, weight))
+        self.position_map[elem] = self.elements
+        self.elements += 1
+        self._bubble_up(elem)
+
+    def extract_min(self) -> Union[int, str]:
+        # Remove and return the element with lowest weight (highest priority)
+        if self.elements > 1:
+            self._swap_nodes(0, self.elements - 1)
+        elem, _ = self.heap.pop()
+        del self.position_map[elem]
+        self.elements -= 1
+        if self.elements > 0:
+            bubble_down_elem, _ = self.heap[0]
+            self._bubble_down(bubble_down_elem)
+        return elem
+
+    def update_key(self, elem: Union[int, str], weight: int) -> None:
+        # Update the weight of the given key
+        position = self.position_map[elem]
+        self.heap[position] = (elem, weight)
+        if position > 0:
+            parent_position = get_parent_position(position)
+            _, parent_weight = self.heap[parent_position]
+            if parent_weight > weight:
+                self._bubble_up(elem)
+            else:
+                self._bubble_down(elem)
+        else:
+            self._bubble_down(elem)
+
+    def _bubble_up(self, elem: Union[int, str]) -> None:
+        # Place a node at the proper position (upward movement) [to be used internally
+        # only]
+        curr_pos = self.position_map[elem]
+        if curr_pos == 0:
+            return
+        parent_position = get_parent_position(curr_pos)
+        _, weight = self.heap[curr_pos]
+        _, parent_weight = self.heap[parent_position]
+        if parent_weight > weight:
+            self._swap_nodes(parent_position, curr_pos)
+            return self._bubble_up(elem)
+        return
+
+    def _bubble_down(self, elem: Union[int, str]) -> None:
+        # Place a node at the proper position (downward movement) [to be used
+        # internally only]
+        curr_pos = self.position_map[elem]
+        _, weight = self.heap[curr_pos]
+        child_left_position = get_child_left_position(curr_pos)
+        child_right_position = get_child_right_position(curr_pos)
+        if child_left_position < self.elements and child_right_position < self.elements:
+            _, child_left_weight = self.heap[child_left_position]
+            _, child_right_weight = self.heap[child_right_position]
+            if child_right_weight < child_left_weight:
+                if child_right_weight < weight:
+                    self._swap_nodes(child_right_position, curr_pos)
+                    return self._bubble_down(elem)
+        if child_left_position < self.elements:
+            _, child_left_weight = self.heap[child_left_position]
+            if child_left_weight < weight:
+                self._swap_nodes(child_left_position, curr_pos)
+                return self._bubble_down(elem)
+        else:
+            return
+        if child_right_position < self.elements:
+            _, child_right_weight = self.heap[child_right_position]
+            if child_right_weight < weight:
+                self._swap_nodes(child_right_position, curr_pos)
+                return self._bubble_down(elem)
+        else:
+            return
+
+    def _swap_nodes(self, node1_pos: int, node2_pos: int) -> None:
+        # Swap the nodes at the given positions
+        node1_elem = self.heap[node1_pos][0]
+        node2_elem = self.heap[node2_pos][0]
+        self.heap[node1_pos], self.heap[node2_pos] = (
+            self.heap[node2_pos],
+            self.heap[node1_pos],
+        )
+        self.position_map[node1_elem] = node2_pos
+        self.position_map[node2_elem] = node1_pos
+
+
+class GraphUndirectedWeighted:
+    """
+    Graph Undirected Weighted Class
+
+    Functions:
+    add_node: function to add a node in the graph
+    add_edge: function to add an edge between 2 nodes in the graph
+    """
+
+    def __init__(self) -> None:
+        self.connections = {}
+        self.nodes = 0
+
+    def __repr__(self) -> str:
+        return str(self.connections)
+
+    def __len__(self) -> int:
+        return self.nodes
+
+    def add_node(self, node: Union[int, str]) -> None:
+        # Add a node in the graph if it is not in the graph
+        if node not in self.connections:
+            self.connections[node] = {}
+            self.nodes += 1
+
+    def add_edge(
+        self, node1: Union[int, str], node2: Union[int, str], weight: int
+    ) -> None:
+        # Add an edge between 2 nodes in the graph
+        self.add_node(node1)
+        self.add_node(node2)
+        self.connections[node1][node2] = weight
+        self.connections[node2][node1] = weight
+
+
+def prims_algo(
+    graph: GraphUndirectedWeighted,
+) -> Tuple[Dict[str, int], Dict[str, Optional[str]]]:
+    """
+    >>> graph = GraphUndirectedWeighted()
+
+    >>> graph.add_edge("a", "b", 3)
+    >>> graph.add_edge("b", "c", 10)
+    >>> graph.add_edge("c", "d", 5)
+    >>> graph.add_edge("a", "c", 15)
+    >>> graph.add_edge("b", "d", 100)
+
+    >>> dist, parent = prims_algo(graph)
+
+    >>> abs(dist["a"] - dist["b"])
+    3
+    >>> abs(dist["d"] - dist["b"])
+    15
+    >>> abs(dist["a"] - dist["c"])
+    13
+    """
+    # prim's algorithm for minimum spanning tree
+    dist = {node: maxsize for node in graph.connections}
+    parent = {node: None for node in graph.connections}
+    priority_queue = MinPriorityQueue()
+    [priority_queue.push(node, weight) for node, weight in dist.items()]
+    if priority_queue.is_empty():
+        return dist, parent
+
+    # initialization
+    node = priority_queue.extract_min()
+    dist[node] = 0
+    for neighbour in graph.connections[node]:
+        if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
+            dist[neighbour] = dist[node] + graph.connections[node][neighbour]
+            priority_queue.update_key(neighbour, dist[neighbour])
+            parent[neighbour] = node
+    # running prim's algorithm
+    while not priority_queue.is_empty():
+        node = priority_queue.extract_min()
+        for neighbour in graph.connections[node]:
+            if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
+                dist[neighbour] = dist[node] + graph.connections[node][neighbour]
+                priority_queue.update_key(neighbour, dist[neighbour])
+                parent[neighbour] = node
+    return dist, parent
+
+
+if __name__ == "__main__":
+    from doctest import testmod
+
+    testmod()