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

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

        color[current_vertex] = BLACK

        for adj_vertex, adj_weight in adj_table[current_vertex].items():
            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 = 0

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

Python: import sys

f_i = sys.stdin

n = int(f_i.readline())

adj = [[]] * n
for x in f_i:
    x = list(map(int, x.split()))
    adj[x[0]] = [c_v for c_v in zip(x[3::2], x[2::2])]

import heapq

def dijkstra():
    PQ = [(0, 0)]
    isVisited = [False] * n
    distance = [999900001] * n

    distance[0] = 0
    
    while PQ:
        uc, u = heapq.heappop(PQ)

        if uc <= distance[u]:
            isVisited[u] = True
        
            for vc, v in adj[u]:
                if isVisited[v] == False:
                    t_cost = distance[u] + vc
                    if t_cost < distance[v]:
                        distance[v] = t_cost
                        heapq.heappush(PQ, (t_cost, v))

    for v, d in enumerate(distance):
        print(v, d)


dijkstra()

Python: import sys

f_i = sys.stdin

n = int(f_i.readline())

adj = [[]] * n
for x in f_i:
    x = list(map(int, x.split()))
    adj[x[0]] = [c_v for c_v in zip(x[3::2], x[2::2])]

import heapq

def dijkstra():
    PQ = [(0, 0)]
    isVisited = [False] * n
    distance = [999900001] * n

    distance[0] = 0
    
    while PQ:
        uc, u = heapq.heappop(PQ)

        if uc > distance[u]:
            continue

        isVisited[u] = True
        
        for vc, v in adj[u]:
            if isVisited[v] == True:
                continue
            t_cost = distance[u] + vc
            if t_cost < distance[v]:
                distance[v] = t_cost
                heapq.heappush(PQ, (t_cost, v))

    for v, d in enumerate(distance):
        print(v, d)


dijkstra()

Python: import sys
inf = float('inf')

def debug(x, table):
    for name, val in table.items():
        if x is val:
            print('DEBUG:{} -> {}'.format(name, val), file=sys.stderr)
            return None

def SSSP(n, Adj):
    d = [inf] * n
    p = [-1] * n
    checked = [False] * n
    d[0] = 0
    checked[0] = True
    u = 0

    for i in range(n - 1):
        for v, cost in Adj[u]:
            if d[u] + cost < d[v]:
                p[v] = u
                d[v] = d[u] + cost

        min_n = 0
        min_w = inf
        for v in range(n):
            if not checked[v] and d[v] < min_w:
                min_n = v
                min_w = d[v]

        u = min_n
        checked[u] = True

    return d

def solve():
    n = int(input())
    Adj = [[] for i in range(n)]

    for i in range(n):
        u, k, *line = [int(i) for i in input().split()]
        for j in range(k):
            Adj[u].append((line[2*j], line[2*j + 1]))

    # debug(Adj, locals())

    d = SSSP(n, Adj)

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

if __name__ == '__main__':
    solve()