Go: package main

import (
	"bufio"
	"fmt"
	"os"
	"strconv"
)

const BIG_COST = 200000

type Path struct {
	node int
	cost int
}

func main() {
	sc := bufio.NewScanner(os.Stdin)
	sc.Split(bufio.ScanWords)

	nNodes := scanInt(sc)

	// 各頂点につながる頂点一覧を得るための隣接リスト
	graph := make([][]Path, nNodes)

	for i := 0; i < nNodes; i++ {
		u := scanInt(sc)
		k := scanInt(sc)
		graph[i] = make([]Path, k)
		for d := 0; d < k; d++ {
			graph[u][d] = Path{
				node: scanInt(sc),
				cost: scanInt(sc),
			}
		}
	}

	// 0 から各頂点への最小コストを保存する配列
	minCosts := make([]int, nNodes)

	minCosts[0] = 0
	for i := 1; i < nNodes; i++ {
		minCosts[i] = BIG_COST
	}

	// 各頂点が調査済みかどうかを保持する配列
	checked := make([]bool, nNodes)

	// 最小コストのパスを高速に取得するためのヒープ。
	// コストが小さいものほどルートに来るようにする。
	heap := NewHeap(func(a, b Path) bool {
		return a.cost < b.cost
	})

	heap.insert(Path{node: 0, cost: 0})
	for heap.size > 0 {
		u := heap.extractMax()

		// 隣接行列を使っていた時とは違い、ヒープには
		// 同じノードへのパスが複数含まれうる。そのため既によりコストの低い
		// パスが見つかっている場合は無視する。
		if minCosts[u.node] < u.cost {
			continue
		}

		checked[u.node] = true

		for _, v := range graph[u.node] {
			cost := v.cost + u.cost
			if !checked[v.node] && cost < minCosts[v.node] {
				minCosts[v.node] = cost
				heap.insert(Path{node: v.node, cost: cost})
			}
		}
	}

	w := bufio.NewWriter(os.Stdout)
	for i, cost := range minCosts {
		fmt.Fprintf(w, "%d %d\n", i, cost)
	}
	w.Flush()
}

func scanInt(sc *bufio.Scanner) int {
	sc.Scan()
	v, _ := strconv.Atoi(sc.Text())
	return v
}

// --- From ALDS1_9_C: Priority Queue

type Heap struct {
	size   int
	heap   []Path
	isHigh func(a, b Path) bool
}

func NewHeap(isHigh func(a, b Path) bool) Heap {
	heap := make([]Path, 1) // 先頭は不使用
	return Heap{size: 0, heap: heap, isHigh: isHigh}
}

func (h *Heap) insert(v Path) {
	h.size += 1
	if h.size > len(h.heap)-1 {
		h.heap = append(h.heap, v)
	} else {
		h.heap[h.size] = v
	}

	for i := h.size; i > 1; {
		parent := i / 2
		if h.isHigh(h.heap[parent], h.heap[i]) {
			break
		}
		h.heap[i], h.heap[parent] = h.heap[parent], h.heap[i]
		i = parent
	}
}

func (h *Heap) extractMax() Path {
	if h.size <= 0 {
		panic("cannot extract from empty heap")
	}

	max := h.heap[1]
	h.heap[1] = h.heap[h.size]
	h.size -= 1
	h.maxHeapify(1)

	return max
}

func (h *Heap) maxHeapify(idx int) {
	left := idx * 2
	right := left + 1
	largest := idx

	if left <= h.size {
		if h.isHigh(h.heap[left], h.heap[idx]) {
			largest = left
		}
		if right <= h.size && h.isHigh(h.heap[right], h.heap[largest]) {
			largest = right
		}
	}

	if largest != idx {
		h.heap[idx], h.heap[largest] = h.heap[largest], h.heap[idx]
		h.maxHeapify(largest)
	}
}

Python: import heapq
from collections import deque
from enum import Enum
import sys
import math
from _heapq import heappush, heappop

#------------------------------------------#

BIG_NUM = 2000000000
HUGE_NUM = 9999999999999999
MOD = 1000000007
EPS = 0.000000001

#------------------------------------------#

class Edge:
    def __init__(self,arg_to,arg_cost):
        self.to = arg_to
        self.cost = arg_cost

class Info:
    def __init__(self,arg_node_id,arg_sum_cost):
        self.node_id = arg_node_id
        self.sum_cost = arg_sum_cost

    def __lt__(self,arg):
        return self.sum_cost < arg.sum_cost


V = int(input())
min_dist = [BIG_NUM]*V
G = [[] for i in range(V)]

for loop in range(V):
    node_id,tmp_num,*table = list(map(int,input().split()))
    for i in range(0,len(table),2):
        G[node_id].append(Edge(table[i],table[i+1]))


min_dist[0] = 0
Q = []

heappush(Q, Info(0,0))

while len(Q) > 0:
    info = heappop(Q)
    if info.sum_cost > min_dist[info.node_id]:
        continue
    for edge in G[info.node_id]:
        if min_dist[edge.to] > info.sum_cost+edge.cost:
            min_dist[edge.to] = info.sum_cost+edge.cost
            heappush(Q, Info(edge.to,min_dist[edge.to]))


for i in range(V):
    print("%d %d"%(i,min_dist[i]))

Python: from heapq import heappush, heappop
from collections import deque

def main():
    V = int(input())
    q = []
    G = [deque() for i in range(V)]
    
    for i in range(V):
        tmp = list(map(int, input().split()))
        
        if tmp[0] == 0:
            for j in range(tmp[1]): heappush(q, (tmp[2+2*j:4+2*j][::-1]))
                
        else:
            for j in range(tmp[1]): G[tmp[0]].append(tmp[2+2*j:4+2*j])
            
    dist = [float('inf')] * V
    visited = [False] * V
    dist[0], visited[0] = 0, True
    
    while q:
        cost, v = heappop(q)
        
        if visited[v]: continue
    
        dist[v], visited[v] = cost, True
        for u, c in G[v]:
            if visited[u] or dist[u] < cost + c: continue
            heappush(q, [c+cost, u])
        
    for i in range(V): print(i, dist[i])
    
if __name__ == '__main__':
    main()

Python: import heapq

n = int(input())
edges = [None] * n
for _ in range(n):
    l = list(map(int, input().split()))
    edges[l[0]] = set((c, j) for j, c in (l[i:i + 2] for i in range(2, l[1] * 2 + 2, 2)))

min_costs = [0] * n
visited = [False] * n
visited[0] = True
queue = list(edges[0])
heapq.heapify(queue)
remains = n - 1

while True:
    total_cost, vertex = heapq.heappop(queue)
    if visited[vertex]:
        continue
    visited[vertex] = True
    min_costs[vertex] = total_cost
    remains -= 1
    if not remains:
        break
    for cost, vertex2 in edges[vertex]:
        if not visited[vertex2]:
            heapq.heappush(queue, (total_cost + cost, vertex2))

for r in enumerate(min_costs):
    print(*r)

Python: import heapq


class ShortestPath:
    """Dijkstra's algorithm : find the shortest path from a vertex
       Complexity: O(E + log(V))
       used in GRL1A(AOJ)
    """

    def __init__(self, V, E, start, INF=10**9):
        """ V: the number of vertexes
            E: adjacency list (all edge in E must be 0 or positive)
            start: start vertex
            INF: Infinity distance
        """
        self.V = V
        self.E = E
        self.dijkstra(start, INF)

    def dijkstra(self, start, INF):
        que = list()
        self.distance = [INF] * self.V  # distance from start
        self.prev = [-1] * self.V  # prev vertex of shortest path
        self.distance[start] = 0
        heapq.heappush(que, (0, start))

        while len(que) > 0:
            dist, fr = heapq.heappop(que)
            if self.distance[fr] < dist:
                continue
            for to, cost in self.E[fr]:
                if self.distance[fr] + cost < self.distance[to]:
                    self.distance[to] = self.distance[fr] + cost
                    heapq.heappush(que, (self.distance[to], to))
                    self.prev[to] = fr

    def getPath(self, end):
        path = [end]
        while self.prev[end] != -1:
            end = self.prev[end]
        return path[::-1]
V = int(input())
edge = [[] for _ in range(V)]
for _ in range(V):
    u, _, *e = map(int, input().split())
    for t, d in zip(e[::2], e[1::2]):
        edge[u].append((t, d))

sp = ShortestPath(V, edge, 0)
for i, d in enumerate(sp.distance):
    print(i, d)