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: from heapq import heapify, heappop, heappush, heappushpop


class PriorityQueue:
    def __init__(self, heap):
        '''
        heap ... list
        '''
        self.heap = heap
        heapify(self.heap)

    def push(self, item):
        heappush(self.heap, item)

    def pop(self):
        return heappop(self.heap)

    def pushpop(self, item):
        return heappushpop(self.heap, item)

    def __call__(self):
        return self.heap

    def __len__(self):
        return len(self.heap)


def dijkstra(graph, s, N):
    '''
    graph...隣接リスト形式 リスト内要素は(ノード, エッジ長)
    s...始点ノード
    N...頂点数

    return
    ----------
    D ... 各点までの最短距離
    P ... 最短経路木における親
    '''
    pq = PriorityQueue([])
    P = [None] * N
    D = [float('inf')] * N
    D[s] = 0
    pq.push((D[s], s))  # (最短距離, 次のノード)
    while pq:
        d, v = pq.pop()
        if D[v] < d:  # この辺を扱っても最短距離にならない
            continue  # is_visitedなくてもこれを使うことで最小のものを再び探索するのを防げる
        for to, cost in graph[v]:
            if D[to] > D[v] + cost:  # v周りにおける最短経路の候補の更新
                D[to] = D[v] + cost
                pq.push((D[to], to))
                P[to] = v
    return D, P

# load datas
N = int(input())
adj = [[] for _ in range(N)]  # (node_id, 隣接要素, 隣接nodeidとコスト)の順で格納する
'''
例
[
(0について) [(1,1235), (4,65)]
(1について) [(20,6000)]
...
]
'''
for _ in range(N):
    tmp = list(map(int, input().split()))
    if tmp[1] != 0:
        node = tmp[0]
        for i in range(2, 2 + tmp[1] * 2, 2):
            adj[node].append((tmp[i], tmp[i + 1]))

D, P = dijkstra(adj, 0,N)

for i in range(N):
    print(i, D[i])

Python: # coding: utf-8

import heapq

def dj(adjm, start):
    n = len(adjm)
    d = [float("inf")] * n
    d[start] = 0
    q = []
    heapq.heappush(q, (0, start))

    while(q):
        td, v = heapq.heappop(q)
        for k in adjm[v].keys():
            nd = td + adjm[v][k]
            if d[k] > nd:
                d[k] = nd
                heapq.heappush(q, (nd, k))
    return d


def main():
    n = int(input())
    # 隣接行列
    adjm = [{} for _ in range(n)]
    for i in range(n):
        vdata = list(map(int, input().split()))
        for j in range(vdata[1]):
            v, c = vdata[j * 2 + 2], vdata[j * 2 + 3]
            adjm[i][v] = c
    ans = dj(adjm, 0)
    for i, d in enumerate(ans):
        print(i, d)


if __name__ == "__main__":
    main()

Python: from collections import defaultdict
import heapq
v_num = int(input())
connect = defaultdict(list)

for _ in range(v_num):
    que = [int(n) for n in input().split(" ")]
    connect[que[0]] = [[v, w] for v, w in zip(que[2:len(que):2], que[3:len(que):2])]
    # weight_edge[que[0]] = {k:v for k, v in zip(que[2:len(que):2], que[3:len(que):2])}
queue = []
heapq.heapify(queue)
heapq.heappush(queue, [0, 0])
distance = [-1 for n in range(v_num)]
went = 0
visited = set()
while went < v_num:
    q = heapq.heappop(queue)
    weight, here = q
    if here in visited:
        continue
    visited |= {here}
    went += 1
    distance[here] = weight
    for conn in connect[here]:
        if conn[0] not in visited:
            heapq.heappush(queue, [weight + conn[1], conn[0]])
for i in range(v_num):
    print(i, distance[i])

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_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
    path_list[init_vertex_index] = 0
    path_heap = []
    # heapq: rank by tuple[0], at here it's d[u]
    hp.heappush(path_heap, (0, init_vertex_index))

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

        color[current_vertex] = BLACK

        current_vertex_adjs = adj_table[current_vertex]
        for adj_vertex, adj_weight in current_vertex_adjs.items():
            # adj_weight = current_vertex_adjs.get(adj_vertex)
            if color[adj_vertex] is not BLACK:
                # alt: d[u] + w[u,v]
                alt_path = path_list[current_vertex] + adj_weight
                if alt_path < path_list[adj_vertex]:
                    # update path_list
                    path_list[adj_vertex] = alt_path
                    # update heap
                    hp.heappush(path_heap, (alt_path, adj_vertex))
                    parent_list[adj_vertex] = current_vertex
                    color[adj_vertex] = 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_table = tuple(dict() for _ in range(vertices_num))
    init_vertex_index = 0

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