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: #!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
input:
5
0 3 2 3 3 1 1 2
1 2 0 2 3 4
2 3 0 3 3 1 4 1
3 4 2 1 0 1 1 4 4 3
4 2 2 1 3 3

output:
0 0
1 2
2 2
3 1
4 3
"""

import sys
import heapq as hp

WHITE, GRAY, BLACK = 0, 1, 2
D_MAX = int(5e10 + 1)


def generate_adj_matrix(v_info):
    for each in v_info:
        v_index, v_adj_length, *v_adj_list = map(int, each)
        # assert len(v_adj_list) == v_adj_length * 2
        for pair in zip(v_adj_list[::2], v_adj_list[1::2]):
            init_adj_matrix[v_index][pair[0]] = pair[1]

    return init_adj_matrix


def dijkstra_path():
    # path search init
    path_list[init_vertex_index] = 0
    path_heap = []
    # heapq: rank by tuple[0], here ranked by d[u]
    hp.heappush(path_heap, (0, init_vertex_index))
    while len(path_heap) >= 1:
        current_vertex_index = hp.heappop(path_heap)[1]

        color[current_vertex_index] = BLACK

        current_vertex_index_info = adj_table[current_vertex_index]
        for adj_vertex_index in current_vertex_index_info.keys():
            current_adj_weight = current_vertex_index_info.get(adj_vertex_index)
            if color[adj_vertex_index] is not BLACK:
                # alt: d[u] + w[u,v]
                alt_path = path_list[current_vertex_index] + current_adj_weight
                if alt_path < path_list[adj_vertex_index]:
                    # update path_list
                    path_list[adj_vertex_index] = alt_path
                    # update heap
                    hp.heappush(path_heap, (alt_path, adj_vertex_index))
                    parent_list[adj_vertex_index] = current_vertex_index
                    color[adj_vertex_index] = GRAY

    return path_list


if __name__ == '__main__':
    _input = sys.stdin.readlines()
    vertices_num = int(_input[0])
    init_vertices_table = map(lambda x: x.split(), _input[1:])
    # assert len(init_vertices_table) == vertices_num

    parent_list, path_list = [-1] * vertices_num, [D_MAX] * vertices_num
    color = [WHITE] * vertices_num
    init_adj_matrix = tuple(dict() for _ in range(vertices_num))
    init_vertex_index = 0

    adj_table = generate_adj_matrix(init_vertices_table)
    ans = dijkstra_path()
    for i, v in enumerate(ans):
        print(i, v)

Python: from heapq import*
I=float("inf")
def m():
 n=int(input())
 A=[]
 for _ in[0]*n:
  e=list(map(int,input().split()))
  A+=[zip(e[2::2],e[3::2])]
 d=[0]+[I]*n
 H=[(0,0)]
 while H:
  u=heappop(H)[1]
  for v,c in A[u]:
   t=d[u]+c
   if d[v]>t:
    d[v]=t
    heappush(H,(t,v))
 print('\n'.join(f'{i} {d[i]}'for i in range(n)))
m()

Python: from heapq import heappop,heappush
INF = float("inf")
white = 0; gray = 1; black =2

def dijkstra(s):
    # 頂点sをrootに設定
    dist[s] = 0 
    heappush(PQ,(0,s))
    color[s] = gray

    while PQ:
        #最短経路木に頂点(最短経路木とつながる最小コストの辺を持つ頂点)を追加
        mincost,u = heappop(PQ)
        color[u] = black
        if dist[u] < mincost: continue # 最短じゃなかったら無視

        # 追加した頂点uの隣接頂点についてsからの最小コストを更新
        for v,cost in adj[u]:
            if color[v] == black: continue # 確定済み
            if dist[v] > dist[u] + cost:
                dist[v] = dist[u] + cost
                heappush(PQ,(dist[v],v))
                color[v] = gray

if __name__ == "__main__":
    n = int(input())
    adj = [[] for _ in range(n)]
    for i in range(n):
        inp = [int(x) for x in input().split()]
        u = inp[0]
        for v,c in zip(inp[2::2],inp[3::2]): adj[u].append((v,c))
    color = [white]*n #各頂点の訪問状態 （white:未訪問 gray:暫定 black:確定）
    dist = [INF]*n # 始点sからの最小コスト
    PQ = []
    dijkstra(0)
    for i in range(n): print("{} {}".format(i,dist[i]))

Python: import heapq
def Dsa(n):
    inf = 10 ** 6 + 1
    dt = [0] + [inf] * (n - 1)
    q = [(0, 0)]
    while q:
        u = heapq.heappop(q)[1]
        for (v, c) in ed[u]:
            alt = dt[u] + c
            if dt[v] > alt:
                dt[v] = alt
                heapq.heappush(q, (alt, v))
    return dt
 
n = int(input())
ed = [[]] * n
for _ in range(n):
    l = list(map(int, input().split()))
    ed[l[0]] = (e for e in zip(l[2::2], l[3::2]))
for i, c in enumerate(Dsa(n)):
    print(i, c)

Python: import heapq

INF = 1e9

n = int(input())
adj_list = [[]] * n

for u in range(n):
    U = list(map(int, input().split()[2:]))
    adj_list[u] = zip(U[0::2], U[1::2]) # adj_list[u] = (v, cost)


def dijkstra(x):
    d = [INF] * x
    d[0] = 0
    hq = [(0, 0)] # (distance, v)??????????????¬??????????????????(key, ????´?)???????????????push, pop?????????
    
    while hq:
        u = heapq.heappop(hq)[1]
        for v, c in adj_list[u]:
            if d[v] > d[u] + c:
                d[v] = d[u] + c
                heapq.heappush(hq, (d[u] + c, v))
                
    return d

for i, di in enumerate(dijkstra(n)):
    print(i,di)