Go: package main

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

const (
	MAX_N      = 10000
	MAX_WEIGHT = 100000
)

type Edge struct {
	start  int
	end    int
	weight int
}

type ShortestInfo struct {
	edges    []Edge
	shortest int
}

type Neighbor struct {
	node   int
	weight int
}

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

	n, err := nextInt(sc)
	if err != nil {
		panic(err)
	}

	shortestList := make([]ShortestInfo, n)
	for i := 1; i < n; i++ {
		shortestList[i] = ShortestInfo{edges: []Edge{}, shortest: MAX_N*MAX_WEIGHT + 1}
	}

	adjacentMatrix, err := createAdjacentMatrix(n, sc)
	if err != nil {
		panic(err)
	}

	sssp([]int{0}, shortestList, adjacentMatrix)
	for _, v := range shortestList {
		fmt.Printf("%d %d\n", v.getEnd(), v.shortest)
	}
}

func (si ShortestInfo) getEnd() int {
	if len(si.edges) == 0 {
		return 0
	} else {
		return si.edges[len(si.edges)-1].end
	}
}

func createAdjacentMatrix(n int, sc *bufio.Scanner) ([][]Neighbor, error) {
	adjacentMatrix := make([][]Neighbor, n)
	for i := 0; i < n; i++ {
		id, err := nextInt(sc)
		if err != nil {
			return nil, err
		}
		adjacentMatrix[id] = []Neighbor{}
		vertexNum, err := nextInt(sc)
		if err != nil {
			return nil, err
		}
		for j := 0; j < vertexNum; j++ {
			v, err := nextInt(sc)
			if err != nil {
				return nil, err
			}
			c, err := nextInt(sc)
			if err != nil {
				return nil, err
			}
			adjacentMatrix[id] = append(adjacentMatrix[id], Neighbor{v, c})
		}
	}
	return adjacentMatrix, nil
}

func sssp(nodeStack []int, shortestList []ShortestInfo, adjacentMatrix [][]Neighbor) {
	for len(nodeStack) > 0 {
		currentNode := nodeStack[0]
		nodeStack = nodeStack[1:]
		currentShortest := getShortest(currentNode, shortestList)
		adjacents := findAdjacents(currentNode, adjacentMatrix)
		for i := 0; i < len(adjacents); i++ {
			adj := adjacents[i]
			if currentShortest.shortest+adj.weight < getShortest(adj.end, shortestList).shortest {
				setShortest(adj.end, adj, currentShortest, shortestList)
				nodeStack = append(nodeStack, adj.end)
			}
		}
	}
}

func getShortest(node int, shortestList []ShortestInfo) ShortestInfo {
	return shortestList[node]
}

func setShortest(node int, edge Edge, currentShortest ShortestInfo, shortestList []ShortestInfo) {
	newEdges := make([]Edge, len(currentShortest.edges)+1)
	for i := 0; i < len(newEdges)-1; i++ {
		newEdges[i] = currentShortest.edges[i]
	}
	newEdges[len(newEdges)-1] = edge
	shortestInfo := ShortestInfo{edges: newEdges, shortest: currentShortest.shortest + edge.weight}
	shortestList[node] = shortestInfo
}

func findAdjacents(currentNode int, adjacentMatrix [][]Neighbor) []Edge {
	adjacents := adjacentMatrix[currentNode]
	edges := []Edge{}
	for _, neighbor := range adjacents {
		edges = append(edges, Edge{start: currentNode, end: neighbor.node, weight: neighbor.weight})
	}
	return edges
}

func nextInt(sc *bufio.Scanner) (int, error) {
	sc.Scan()
	return strconv.Atoi(sc.Text())
}

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()

Python: import sys
from collections import defaultdict
from typing import Dict


def find_shortest_distance(start_vertex: int, n: int,  relations: Dict[int, Dict]) -> Dict[int, int]:
    unreached_vertices = [x for x in range(1, n)]
    distance = {x: sys.maxsize for x in range(n)}
    distance[start_vertex] = 0
    vtx = None
    tmp_distance = [sys.maxsize for x in range(n)]
    for k, v in relations[start_vertex].items():
        tmp_distance[k] = v

    while True:
        min_dist = sys.maxsize
        for i in unreached_vertices:
            dist = tmp_distance[i]
            if dist < min_dist:
                min_dist = dist
                vtx = i
        if min_dist == sys.maxsize:
            break
        unreached_vertices.remove(vtx)
        distance[vtx] = min_dist
        neighbor_vertices = relations[vtx]
        for n_vtx, n_dist in neighbor_vertices.items():
            n_dist_from_start = distance[vtx] + n_dist
            tmp_dist = tmp_distance[n_vtx]
            if tmp_dist == sys.maxsize:
                tmp_distance[n_vtx] = n_dist_from_start
            else:
                if n_dist_from_start < tmp_dist:
                    tmp_distance[n_vtx] = n_dist_from_start
    return distance


def main():
    n = int(input())
    relations = defaultdict(dict)

    lines = sys.stdin.readlines()
    for i in range(n):
        u, k, *vtx_wght = map(int, lines[i].split())
        for v_i in range(k):
            relations[u][vtx_wght[2*v_i]] = vtx_wght[2*v_i+1]

    dist_from_0 = find_shortest_distance(0, n, relations)

    answers = [None] * n
    sorted_dist = sorted(dist_from_0.items(), key=lambda x: x[0])
    for i in range(n):
        answers[i] = f"{sorted_dist[i][0]} {sorted_dist[i][1]}"
    [print(ans) for ans in answers]
    return


main()

Python: #!/usr/bin/python3
from heapq import heappop, heappush
n = int(input())
inf = 100 * 100000

M = [[] for i in range(n)]

for i in range(n):
    L = list(map(int, input().split()))
    u = L[0]
    k = L[1]
    v = L[2::2]
    c = L[3::2]
    for j in range(k):
        M[u].append((v[j], c[j]))
result = [0] * n
S = set()
V = set(range(n))
S.add(0)
d = []
for m in M[0]:
    heappush(d, (m[1], m[0]))
T = V - S

while len(T) != 0:
    c, v = heappop(d)
    while v in S:
        c, v = heappop(d)
    result[v] = c
    S.add(v)
    T.remove(v)
    for m in M[v]:
        if m[0] in T:
            heappush(d, (result[v] + m[1], m[0]))

for i, v in enumerate(result):
    print(i, v)