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: #!/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 v_detail in v_info:
        v_index = int(v_detail[0])
        v_adj_length = int(v_detail[1])
        v_adj_list = v_detail[2:]
        # 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]] = int(v_adj_list[j + 1])

    return init_adj_matrix


def dijkstra_path():
    start_vertex_index = 0
    path_list[start_vertex_index] = 0
    path_heap = []
    # heapq: compare by tuple[0]
    hp.heappush(path_heap, (path_list[start_vertex_index], start_vertex_index))
    while len(path_heap) >= 1:
        current_vertex = hp.heappop(path_heap)[1]

        color[current_vertex] = BLACK

        for adj_vertex in range(vertices_num):
            current_adj_weight = adj_matrix[current_vertex].get(str(adj_vertex), -1)
            if current_adj_weight < 0:
                continue
            elif color[adj_vertex] is not BLACK:
                if path_list[current_vertex] + current_adj_weight < path_list[adj_vertex]:
                    # update path_list
                    path_list[adj_vertex] = path_list[current_vertex] + current_adj_weight
                    # update heap
                    hp.heappush(path_heap, (path_list[adj_vertex], 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 = list(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 j in range(vertices_num):
        print(j, ans[j])

Python: from heapq import *
n = int(input())
INF = float("inf")
adj = [[] for _ in range(10**4+4)]
for _ in range(n):
    a,*b = map(int,input().split())
    if b[0] != 0:
        c = b[1:]
        for i in range(len(c)//2):
            k,w = c[2*i],c[2*i+1]
            adj[a].append((k,w))
H = []
heapify(H)
searched = [0]*n
d = [INF]*n
d[0] = 0
heappush(H, (0,0))
while H:
    f = heappop(H)
    u = f[-1]
    searched[u] = 1
    if d[u] < f[0]:
        continue
    for j in range(len(adj[u])):
        v = adj[u][j][0]
        if searched[v]:
            continue
        if d[v] > d[u]+adj[u][j][-1]:
            d[v] = d[u]+adj[u][j][-1]
            heappush(H,(d[v],v))
for i in range(n):
    ans = -1 if d[i] == INF else d[i]
    print(i,ans)

Python: from collections import deque

n = int(input())
g = []
d = [10**10] * n
d[0] = 0

for i in range(n):
    temp = list(map(int, input().split()))
    k = temp[1]
    temp = temp[2:]
    adj = []
    for j in range(k):
        v, c = temp[j*2], temp[j*2+1]
        adj.append([v, c])
    g.append(adj)

# for i in range(n):
#     print(g[i])

q = deque()
# Add tupple of prev and adj into Queue
q.append((0, g[0]))
done = set()

while q:
    node = q.popleft()
    prev = node[0]
    adj = node[1]
    # print('\nadj:', adj)

    for next in adj:
        v = next[0]
        w = next[1]
        # print('v:', v)
        # print('prev:', prev)

        # d[v] = min(d[v], d[prev] + w)
        if d[prev] + w < d[v]:
            d[v] = d[prev] + w
            # Need to add to Queue regardless of it's in done or not,
            # because the shortest path was updated.
            q.append((v, g[v]))
        if v not in done:
            done.add(v)
            q.append((v, g[v]))

# print('d:', d)

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

Python: from heapq import*
n=int(input())
A=[[]for _ in[0]*n]
for _ in[0]*n:
 e=list(map(int,input().split()))
 for i in range(e[1]):k=2*-~i;A[e[0]]+=[(e[k],e[k+1])]
H=[[0,0]]
d=[0]+[1e6]*n
c=[1]*n
while H:
 f=heappop(H)
 u=f[1]
 c[u]=0
 if d[u]>=f[0]:
  for s in A[u]:
   v=s[0]
   if c[v]and d[v]>d[u]+s[1]:
    d[v]=d[u]+s[1]
    heappush(H,[d[v],v])
for i in range(n):print(i,d[i])