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: V=int(input())

G=[[] for i in range(V)]
d=[1001001001 for i in range(V)]

import heapq

def dijkstra(s):
    q=[]
    d[s]=0
    heapq.heappush(q,(0, s))
    
    while(len(q)):
        
        p=heapq.heappop(q)
        v=p[1]
        if d[v]<p[0]:
            continue
        for e in G[v]:
            if d[e[0]]>d[v]+e[1]:
                d[e[0]]=d[v]+e[1]
                
                heapq.heappush(q, (d[e[0]], e[0]))

import sys
for l in sys.stdin:
    l=list(map(int,l.split()))
    u=l[0]
    k=l[1]
    G[u]=zip(*[iter(l[2:])]*2)
    
dijkstra(0)
for i in range(V):
    print(i, d[i])

Python: # -*- coding: utf-8 -*-
from heapq import heapify, heappop, heappush
 
N = int(input())
INF = float("inf")
d = [INF for n in range(N)]
d[0] = 0
adj = [[] for _ in range(N)]
 
searched = [False for _ in range(N)]
searched[0] = True
 
for n in range(N):
    inp = tuple(map(int, input().split()))
    assert n == inp[0]
    adj[n] = list(zip(inp[2::2], inp[3::2]))
 
heap = [[0, 0]]
 
while len(heap):
    pcost, p = heappop(heap)
    searched[p] = True
     
    if d[p] < pcost:
        continue
     
    for v, c in adj[p]:
        if not searched[v]:
            new_cost = pcost + c
            if new_cost < d[v]:
                d[v] = new_cost
                heappush(heap, [new_cost, v])
 
for n in range(N):
    print("{} {}".format(n, d[n]))

Python: import heapq

def main():

    INF = 1000000009
    n = int(input())
    e = [[] for _ in range(n)]
    for i in range(n):
        inf = list(map(int,input().split()))
        for j in range((inf[1])):
            e[inf[0]].append([inf[2+j*2],inf[2+j*2+1]])
    
    dp = [INF]*n
    dp[0] = 0
    pq = [[0,0]]
    heapq.heapify(pq)
    while pq:
        cost,v = heapq.heappop(pq)
        if dp[v]<cost:continue

        for i in range(len(e[v])):
            u = e[v][i][0]
            ncost = cost+e[v][i][1]
            if ncost < dp[u]:
                dp[u] = ncost
                heapq.heappush(pq,[ncost,u])
    for i in range(n):
        print(i,dp[i])


if __name__ == '__main__':
    main()

Python: # -*- coding: utf-8 -*-
from heapq import heappush, heappop

N = int(input())
INF = float("inf")
adj = []
for _ in range(N):
    inp = tuple(map(int, input().split()))
    adj.append(tuple(zip(inp[2::2], inp[3::2])))

checked = [False for _ in range(N)]
d = [float("inf") for _ in range(N)]
d[0] = 0

heap = [[0, 0]] # cost, vec

while heap:
    c, u = heappop(heap)
    if d[u] < c or checked[u]:
        continue
    for nu, nc in adj[u]:
        new_d = d[u] + nc
        if new_d < d[nu]:
            d[nu] = new_d
            heappush(heap, [d[nu], nu])
    checked[u] = True

for n in range(N):
    print("{} {}".format(n, d[n]))

Python: from heapq import heappush, heappop
from typing import List, Tuple
import sys


if __name__ == "__main__":
    num_vertices = int(input())
    adj_list: List[List[Tuple[int, int]]] = [[] for _ in range(num_vertices)]

    for _ in range(num_vertices):
        vertex, adj_num, *adjs = map(lambda x: int(x), input().split())
        for i in range(adj_num):
            # (cost, adj)
            adj_list[vertex].append((adjs[2 * i + 1], adjs[2 * i]))

    distances: List[int] = [sys.maxsize] * num_vertices
    distances[0] = 0
    que: List[Tuple[int, int]] = []
    heappush(que, (0, 0))

    while [] != que:
        base_distance, parent = heappop(que)
        for cost, adj in adj_list[parent]:
            distance = base_distance + cost
            if distance < distances[adj]:
                distances[adj] = distance
                heappush(que, (distance, adj))

    for vertex, distance in enumerate(distances):
        print(f"{vertex} {distance}")