Go: package main

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

type Item struct {
	value    int // The value of the item; arbitrary.
	priority int // The priority of the item in the queue.
	// The index is needed by update and is maintained by the heap.Interface methods.
	index int // The index of the item in the heap.
}
type PriorityQueue []*Item

func (pq PriorityQueue) Len() int { return len(pq) }
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 = i
	pq[j].index = j
}
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
}

type Q struct {
	pq *PriorityQueue
	ds map[int]int // It has always same value as pq. For random access.
}

func newQ() *Q {
	pq := new(PriorityQueue)
	heap.Init(pq)
	return &Q{
		pq: pq,
		ds: make(map[int]int),
	}
}
func (q *Q) pop() (u, d int) {
	item := heap.Pop(q.pq).(*Item)
	delete(q.ds, item.value)
	return item.value, item.priority
}
func (q *Q) get(u int) (int, bool) { // returns d
	d, ok := q.ds[u]
	return d, ok
}
func (q *Q) set(u, d int) {
	q.ds[u] = d
	for _, item := range *q.pq {
		if item.value != u {
			continue
		}

		item.priority = d
		heap.Fix(q.pq, item.index)
		return
	}
	heap.Push(q.pq, &Item{
		priority: d,
		value:    u,
	})
}
func (q *Q) len() int {
	return len(q.ds)
}

func main() {
	r := bufio.NewReader(os.Stdin)

	var n int
	fmt.Fscan(r, &n)
	cs := make([]map[int]int, n) // []v_i -> c_i
	for h := 0; h < n; h++ {
		var u, k int
		fmt.Fscan(r, &u, &k)
		m := make(map[int]int)
		for i := 0; i < k; i++ {
			var v, c int
			fmt.Fscan(r, &v, &c)
			m[v] = c
		}

		cs[u] = m
	}

	q := newQ()
	q.set(0, 0)
	visited := make(map[int]struct{})
	out := make([]int, n)

	for q.len() > 0 {
		u, d := q.pop()
		for uu, c := range cs[u] {
			// Consider the connection between u and uu.
			if _, ok := visited[uu]; ok {
				continue
			}
			dd := d + c
			if d, ok := q.get(uu); !ok || d > dd {
				q.set(uu, dd)
			}
		}

		out[u] = d
		visited[u] = struct{}{}
	}

	for u, d := range out {
		fmt.Println(u, d)
	}
}

Python: import sys, os, math, bisect, itertools, collections, heapq, queue, copy, array

# from scipy.sparse.csgraph import csgraph_from_dense, floyd_warshall
# from decimal import Decimal
# from collections import defaultdict, deque

sys.setrecursionlimit(10000000)

ii = lambda: int(sys.stdin.buffer.readline().rstrip())
il = lambda: list(map(int, sys.stdin.buffer.readline().split()))
fl = lambda: list(map(float, sys.stdin.buffer.readline().split()))
iln = lambda n: [int(sys.stdin.buffer.readline().rstrip()) for _ in range(n)]

iss = lambda: sys.stdin.buffer.readline().decode().rstrip()
sl = lambda: list(map(str, sys.stdin.buffer.readline().decode().split()))
isn = lambda n: [sys.stdin.buffer.readline().decode().rstrip() for _ in range(n)]

lcm = lambda x, y: (x * y) // math.gcd(x, y)

MOD = 10 ** 9 + 7
INF = float('inf')


def main():
    if os.getenv("LOCAL"):
        sys.stdin = open("input.txt", "r")

    N = ii()

    # (距離, 接する頂点)の情報を持つ有向グラフのデータを生成
    graph = [[] for _ in range(N)]
    for _ in range(N):
        v1, k, *vertices = il()
        for idx in range(0, k * 2, 2):
            v2, c = vertices[idx:idx + 2]
            graph[v1].append((c, v2))

    # ダイクストラ法
    dist = [INF] * N    # 各頂点までの距離を保持する配列
    dist[0] = 0         # 開始始点の頂点は距離をゼロとする
    visited = [0] * N   # 各頂点の訪問状態を保持する配列[0: 未訪問, 1: 訪問済み]
    heap = [(0, 0)]     # 距離と始点頂点（自分自身への移動距離はゼロ）
    heapq.heapify(heap) # 距離と頂点の情報を距離で優先度付きキューとする
    while heap:
        # 頂点uとuに到達するまでの距離
        min_cost, u = heapq.heappop(heap)

        # さらに小さい距離で既に到達済みの場合はcontinue
        if dist[u] < min_cost: continue
        visited[u] = 1  # 頂点uを訪問済みにする

        # グラフの情報から接する頂点と移動距離を順に処理
        for cost, v in graph[u]:
            if visited[v] != 1:
                if dist[u] + cost < dist[v]:
                    # 未到達かつ小さい距離で移動できる場合には
                    # 距離の更新と優先度付きキューに(距離, 頂点)の情報を追加
                    dist[v] = dist[u] + cost
                    heapq.heappush(heap, (dist[v], v))
    # 回答
    for n in range(N):
        print(n, dist[n])


if __name__ == '__main__':
    main()

Python: from collections import defaultdict
import heapq
n = int(input())
global S, Q, l
S = [0]
Q = [(0, 0)]
l = [1000000 for _ in range(n)]
l[0] = 0
w_graph = [defaultdict(int) for _ in range(n)]
graph = [[] for _ in range(n)]
G = [None] * n
def input_graph(n):
  for i in range(n):
    node_id, k, *v_list = list(map(int, input().split()))
    G[i] = [(v, c) for v, c in zip(v_list[::2], v_list[1::2])]

heapq.heapify(Q)
def dijkstra():
  c = 0
  while Q and c < 10:
    u_c, u = heapq.heappop(Q)
    for v, v_c in G[u]:
      """if v not in S:
        S.append(v)
        l[v] = l[u] + v_c
        heapq.heappush(Q, (l[v], v))"""
      if  (l[v] > l[u] + v_c):
        l[v] = l[u] + v_c
        heapq.heappush(Q, (l[v], v))


if __name__ == "__main__":
  input_graph(n)
  dijkstra()
  for i, c in enumerate(l):
    print(i, c)

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_detail = map(int, each)
        v_index, v_adj_length, *v_adj_list = v_detail
        # assert len(v_adj_list) == v_adj_length * 2
        for j in range(0, v_adj_length * 2, 2):
            init_adj_matrix[v_index][v_adj_list[j]] = v_adj_list[j + 1]

    return init_adj_matrix


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

        color[current_vertex_index] = BLACK

        for adj_vertex_index in range(vertices_num):
            current_adj_weight = adj_matrix[current_vertex_index].get(adj_vertex_index, -1)
            if current_adj_weight < 0:
                continue
            elif color[adj_vertex_index] is not BLACK:
                # 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))
    adj_matrix = generate_adj_matrix(init_vertices_table)
    ans = dijkstra_path()
    for i, v in enumerate(ans):
        print(i, v)