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
from __future__ import division, print_function
from sys import stdin, maxint


def main():
    num = int(stdin.readline())

    edges = []
    for _ in xrange(num):
        ns = [int(s) for s in stdin.readline().split()]
        for i in xrange(ns[1]):
            edges.append((ns[0], ns[2*i+2], ns[2*i+3]))

    dis = [maxint] * num
    dis[0] = 0

    while 1:
        update = False
        for from_, to, cost in edges:
            if dis[from_] < maxint and dis[to] > cost + dis[from_]:
                dis[to] = cost + dis[from_]
                update = True

        if not update:
            break

    for i, d in enumerate(dis):
        print(i, d)


main()

Python: def main():
    N = int(input())
    G = [[] for _ in range(N)]
    for _ in range(N):
        u, k, *vc = (int(i) for i in input().split())
        for i in range(0, len(vc), 2):
            G[u].append((vc[i], vc[i+1]))
    d = [[(1 << 63)-1, i] for i in range(N)]
    from heapq import heapify, heappop, heappush

    def dijkstra(s):
        d[s][0] = 0
        que = [(d[s][0], s)]
        heapify(que)
        finished = set()
        while len(finished) < N:
            u = heappop(que)
            while u[1] in finished:
                u = heappop(que)
            if d[u[1]][0] < u[0]:
                print('yeah')
            for v, c in G[u[1]]:
                if v in finished:
                    continue
                if d[v][0] > u[0] + c:
                    d[v][0] = u[0] + c
                    heappush(que, (d[v][0], v))
            finished.add(u[1])
    dijkstra(0)
    for i, val in enumerate(d):
        print(i, val[0])


if __name__ == '__main__':
    main()

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


class SingleShortestPath(object):
    __slots__ = ('adj_table', 'distance', 'parent', 'visited')

    def __init__(self, in_data):
        """

        :param in_data:
        """
        v_num = int(in_data[0])
        vertices = map(lambda x: x.split(), in_data[1:])
        adj_table = tuple(dict() for _ in range(v_num))

        for v_info in vertices:
            v_index, v_adj_length, *v_adj_list = map(int, v_info)
            # assert len(v_adj_list) == v_adj_length * 2
            for source, target in zip(v_adj_list[::2], v_adj_list[1::2]):
                adj_table[v_index][source] = target

        self.adj_table = adj_table
        self.distance = [float('inf')] * v_num
        self.parent = [-1] * v_num
        self.visited = [WHITE] * v_num

    def path_heap_Dijkstra(self, root):
        # path search init
        self.distance[root] = 0
        path_heap = list()
        # heapq: rank by tuple[0], at here it's d[u]
        hp.heappush(path_heap, (0, root))

        while path_heap:
            current = hp.heappop(path_heap)[1]
            self.visited[current] = BLACK

            for adj, cost in self.adj_table[current].items():
                if self.visited[adj] is not BLACK:
                    # alt: d[u] + w[u,v]
                    alt_path = self.distance[current] + cost
                    if alt_path < self.distance[adj]:
                        # update path_list
                        self.distance[adj] = alt_path
                        # update heap
                        hp.heappush(path_heap, (alt_path, adj))
                        self.visited[adj] = GRAY
                        self.parent[adj] = current

        return self.distance


if __name__ == '__main__':
    _input = sys.stdin.readlines()
    case = SingleShortestPath(in_data=_input)
    ans = case.path_heap_Dijkstra(root=0)
    for i, v in enumerate(ans):
        print(i, v)