Go: package main

import (
	"bufio"
	"container/heap"
	"fmt"
	"os"
	"strconv"
)

//make priority queue
type Item struct {
	id       int
	priority int // this state is priority and distans from start
	index    int
}

type PriorityQueue []*Item

func (pq PriorityQueue) Len() int { return len(pq) }

// we want the ascending-priority-queue.
// So we use less than here.
func (pq PriorityQueue) Less(i, j int) bool {
	return pq[i].priority < pq[j].priority
}

func (pq PriorityQueue) Swap(i, j int) {
	pq[i], pq[j] = pq[j], pq[i]
	pq[i].index = j
	pq[j].index = i
}

func (pq *PriorityQueue) Push(x interface{}) {
	n := len(*pq)
	item := x.(*Item)
	item.index = n
	*pq = append(*pq, item)
}

func (pq *PriorityQueue) Pop() interface{} {
	old := *pq
	n := len(old)
	item := old[n-1]
	item.index = -1 //for safety
	*pq = old[0 : n-1]
	return item
}

func (pq *PriorityQueue) update(dist int, id int) {
	for _, i := range *pq {
		if i.id == id {
			if i.priority > dist {
				i.priority = dist
			}
		}
	}
}

// the main code
func nextInt(sc *bufio.Scanner) int {
	sc.Scan()
	n, _ := strconv.Atoi(sc.Text())
	return n
}

func dikstra(dist [][]int, n int) []int {
	// preparate priority queue
	inf := 1 << 30
	pq := make(PriorityQueue, n)
	shortests := make([]int, n)
	for i := 0; i < n; i++ {
		pq[i] = &Item{
			id:       i,
			priority: inf,
			index:    i,
		}

		shortests[i] = inf
	}
	pq[0].priority = 0
	shortests[0] = 0

	for pq.Len() > 0 {
		item := heap.Pop(&pq).(*Item)
		shortests[item.id] = item.priority
		for id, d := range dist[item.id] {
			if d != -1 {
				pq.update(item.priority+d, id)
			}
		}
		heap.Init(&pq)
	}
	return shortests
}

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

	// init
	n := nextInt(sc)
	dist := make([][]int, n)
	for i := 0; i < n; i++ {
		dist[i] = make([]int, n)
		for j := 0; j < n; j++ {
			dist[i][j] = -1
		}
	}

	// std Input
	for i := 0; i < n; i++ {
		u := nextInt(sc)
		k := nextInt(sc)
		for j := 0; j < k; j++ {
			v, c := nextInt(sc), nextInt(sc)
			dist[u][v] = c
		}
	}

	shortests := dikstra(dist, n)
	for i, d := range shortests {
		fmt.Printf("%d %d\n", i, d)
	}
}

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


def generate_adj_table(v_table):
    for each in v_table:
        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_table[v_index][pair[0]] = pair[1]

    return init_adj_table


def dijkstra_path_heap():
    # path search init
    distance[init_vertex] = 0
    path_heap = []
    # heapq: rank by tuple[0], at here it's d[u]
    hp.heappush(path_heap, (0, init_vertex))

    while path_heap:
        current_vertex = hp.heappop(path_heap)[1]

        color[current_vertex] = BLACK

        for adj_vertex, adj_weight in adj_table[current_vertex].items():
            if color[adj_vertex] is not BLACK:
                # alt: d[u] + w[u,v]
                alt_path = distance[current_vertex] + adj_weight
                if alt_path < distance[adj_vertex]:
                    # update path_list
                    distance[adj_vertex] = alt_path
                    # update heap
                    hp.heappush(path_heap, (alt_path, adj_vertex))
                    color[adj_vertex] = GRAY

    return distance


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

    distance = [float('inf')] * vertices_num
    color = [WHITE] * vertices_num
    init_adj_table = tuple(dict() for _ in range(vertices_num))
    init_vertex = 0

    adj_table = generate_adj_table(init_vertices_table)
    ans = dijkstra_path_heap()
    for i, v in enumerate(ans):
        print(i, v)

Python: def dijkstra(n):
    inf = 10 ** 6 + 1
    dist = [0] + [inf] * (n - 1)
    visited = [False] * n

    while not all(visited):
        dmin = inf
        for i, (d, v) in enumerate(zip(dist, visited)):
            if v:
                continue
            elif dmin > d:
                dmin, u = d, i
        visited[u] = True
        for (v, c) in edge[u]:
            if dist[v] > dist[u] + c:
                dist[v] = dist[u] + c
    return dist

n = int(input())
edge = [[]] * n
for _ in range(n):
    l = list(map(int, input().split()))
    edge[l[0]] = (e for e in zip(l[2::2], l[3::2]))
for i, c in enumerate(dijkstra(n)):
    print(i, c)

Python: import sys
input = sys.stdin.buffer.readline

import heapq

#点の数が10**5のオーダー以下で使える
#頂点sからの各頂点への最短距離を出力
def dijkstra(s, n, edge): #s:始点、n:頂点の数、edge:重み付き辺の配列（edge[i]:iから出る[重み、行き先]の配列)
    INF = 10**20
    d = [INF]*n # d[i]: sと点iとの距離
    used = [True]*n #Trueは未確定
    d[s] = 0 #初期条件
    used[s] = False # 初期条件
    edge_list = []
    for a, b in edge[s]:
        heapq.heappush(edge_list, a*(10**6)+b) #heapqの中に重みと辺の抱き合わせた情報を入れる
    while len(edge_list):
        min_edge = heapq.heappop(edge_list) #まだ使われていない頂点の中から最小の重みのものを取りだす
        if not used[min_edge%(10**6)]: # 確定していたらcontinue、確定していない時のみ以下の処理を行う
            continue
        v = min_edge%(10**6) #行き先の情報を復元
        d[v] = min_edge // (10**6) #重みの情報を復元し、sとvの最短距離d[v]を更新する
        used[v] = False
        for e in edge[v]:
            if used[e[1]]: #その行き先がまだ未確定の場合
                heapq.heappush(edge_list, (e[0]+d[v])*(10**6) + e[1])
        
    return d


n = int(input())

edge = [[] for _ in range(n)] # edge:重み付き辺の配列（edge[i]:iから出るtuple(重み、行き先)の配列)
for _ in range(n):
    x, y, *li = list(map(int, input().split()))
    for i in range(y):
        edge[x].append((li[2*i+1], li[2*i]))
        #edge[li[2*i]].append([li[2*i+1], x]) # 無向グラフのとき

d = dijkstra(0, n, edge)

for i in range(n):
    print(i, d[i])

Python: from queue import PriorityQueue
 
INF=1<<30
n=int(input())
adj=[[]for i in range(n+1)]
maxx = 10010

def dijkstra():
    global adj
    PQ=PriorityQueue()
      
    d=[INF for i in range(n)]
    #p=[-1 for i in range(n)]
    flag=[0 for i in range(n)]
 
    d[0] = 0;
    PQ.put((0, 0))
    while (not PQ.empty()):
        f = PQ.get()
        u = f[1]
        
        # 标记已经加入S
        flag[u] = 1

        #如果不是最小最短路径则忽略
        #if (d[u] >f[0] ):
            #continue
        lens = len(adj[u])
        for j in range(lens):
            v = adj[u][j][0]
            if (flag[v] == 0 and d[v] > d[u] + adj[u][j][1]):
                d[v] = d[u] + adj[u][j][1]
                PQ.put((d[v], v));
            

    for i in range(n):
        if d[i]==INF:
            print("%d %d"%(i, -1))
        else:
            print("%d %d"%(i, d[i]))
        

for i in range(n):
    s=[int(x) for x in input().split()]
    u=s[0]
    k=s[1]
    for j in range(k):
        adj[u].append((s[j*2+2],s[2*j+3]))
 
dijkstra()