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
	heap.Init(&pq)

	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


class SingleShortestPath(object):
    __slots__ = ('adj_table', 'distance', 'parent', 'visited')

    def __init__(self, in_data):
        """

        :param in_data:
        """
        v_num = int(in_data[0])
        vertices = map(lambda x: x.split(), in_data[1:])
        adj_table = tuple(dict() for _ in range(v_num))

        for v_info in vertices:
            v_index, v_adj_length, *v_adj_list = map(int, v_info)
            # assert len(v_adj_list) == v_adj_length * 2
            for source, target in zip(v_adj_list[::2], v_adj_list[1::2]):
                adj_table[v_index][source] = target

        self.adj_table = adj_table
        self.distance = [float('inf')] * v_num
        self.parent = [-1] * v_num
        self.visited = [WHITE] * v_num

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

        while path_heap:
            current = hp.heappop(path_heap)[1]
            self.visited[current] = BLACK

            for adj, cost in self.adj_table[current].items():
                if self.visited[adj] is not BLACK:
                    # alt: d[u] + w[u,v]
                    alt_path = self.distance[current] + cost
                    if alt_path < self.distance[adj]:
                        # update path_list
                        self.distance[adj] = alt_path
                        # update heap
                        hp.heappush(path_heap, (alt_path, adj))
                        self.visited[adj] = GRAY
                        self.parent[adj] = current

        return self.distance


if __name__ == '__main__':
    _input = sys.stdin.readlines()
    case = SingleShortestPath(in_data=_input)
    ans = case.path_heap_Dijkstra(root=0)
    for i, v in enumerate(ans):
        print(i, v)

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(1e7 + 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 not current_adj_weight:
                continue
            elif 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: 
n=int(input())
graph=[[0]*n for i in range(n)]


#graph[i][j]は、頂点iから頂点jへの枝の重みを表す(0は枝なし)
for loop in range(n):
    tmp_ope=list(map(int,input().split()))
    for j in range(1,tmp_ope[1]+1):
        graph[tmp_ope[0]][tmp_ope[2*j]]=tmp_ope[2*j+1]

#S[i]＝１の時、頂点iは走査済み
S=[0]*n
#d[i]はSに含まれる点のみを使って、頂点iにたどり着くときの最短パス
d=[999999]*n
d[0]=0

for loop in range(n):
    tmp_vertex=-1

    #tmp_vertexは走査済みでない点の中で、d[i]が最小となるインデックス
    for j in range(n):
        if(S[j]==0):
            if(tmp_vertex!=-1):
                if(d[j] < d[tmp_vertex]):
                    tmp_vertex=j
            else:
                tmp_vertex=j
    
    
    S[tmp_vertex]=1

    #dを更新
    for j in range(n):
        if(graph[tmp_vertex][j]!=0 and S[j]==0):
            d[j]=min([ d[j], d[tmp_vertex] + graph[tmp_vertex][j] ])

for i in range(n):
    print(f"{i} {d[i]}")

Python: from heapq import*
I=float("inf")
def m():
 n=int(input())
 A=[[]]*n
 for _ in range(n):
  e=list(map(int,input().split()))
  A[e[0]]=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))
 for i in range(n):print(i,d[i])
m()