Go: package main

import (
	"bufio"
	"container/heap"
	"fmt"
	"os"
	"strconv"
)

//make priority queue
type Item struct {
	id       int
	priority int // this state is priority and distans from start
	index    int
}

type PriorityQueue []*Item

func (pq PriorityQueue) Len() int { return len(pq) }

// we want the ascending-priority-queue.
// So we use less than here.
func (pq PriorityQueue) Less(i, j int) bool {
	return pq[i].priority < pq[j].priority
}

func (pq PriorityQueue) Swap(i, j int) {
	pq[i], pq[j] = pq[j], pq[i]
	pq[i].index = j
	pq[j].index = i
}

func (pq *PriorityQueue) Push(x interface{}) {
	n := len(*pq)
	item := x.(*Item)
	item.index = n
	*pq = append(*pq, item)
}

func (pq *PriorityQueue) Pop() interface{} {
	old := *pq
	n := len(old)
	item := old[n-1]
	item.index = -1 //for safety
	*pq = old[0 : n-1]
	return item
}

func (pq *PriorityQueue) update(dist int, id int) {
	for _, i := range *pq {
		if i.id == id {
			if i.priority > dist {
				i.priority = dist
			}
		}
	}
}

// the main code
func nextInt(sc *bufio.Scanner) int {
	sc.Scan()
	n, _ := strconv.Atoi(sc.Text())
	return n
}

func dikstra(dist [][]int, n int) []int {
	// preparate priority queue
	inf := 1 << 30
	pq := make(PriorityQueue, n)
	shortests := make([]int, n)
	for i := 0; i < n; i++ {
		pq[i] = &Item{
			id:       i,
			priority: inf,
			index:    i,
		}

		shortests[i] = inf
	}
	pq[0].priority = 0
	shortests[0] = 0
	heap.Init(&pq)

	for pq.Len() > 0 {
		item := heap.Pop(&pq).(*Item)
		shortests[item.id] = item.priority
		for id, d := range dist[item.id] {
			if d != -1 {
				pq.update(item.priority+d, id)
			}
		}
		heap.Init(&pq)
	}
	return shortests
}

func main() {
	sc := bufio.NewScanner(os.Stdin)
	sc.Split(bufio.ScanWords)

	// init
	n := nextInt(sc)
	dist := make([][]int, n)
	for i := 0; i < n; i++ {
		dist[i] = make([]int, n)
		for j := 0; j < n; j++ {
			dist[i][j] = -1
		}
	}

	// std Input
	for i := 0; i < n; i++ {
		u := nextInt(sc)
		k := nextInt(sc)
		for j := 0; j < k; j++ {
			v, c := nextInt(sc), nextInt(sc)
			dist[u][v] = c
		}
	}

	shortests := dikstra(dist, n)
	for i, d := range shortests {
		fmt.Printf("%d %d\n", i, d)
	}
}

Python: # -*- coding: utf-8 -*-
"""
http://judge.u-aizu.ac.jp/onlinejudge/description.jsp?id=ALDS1_12_C

???????§????????????????II
"""
from enum import Enum
from heapq import heappush, heappop, heapify
import itertools


class Priority_queue():
    # https://docs.python.jp/3/library/heapq.html
    def __init__(self):
        self.pq = []                         # list of entries arranged in a heap
        self.entry_finder = {}               # mapping of tasks to entries
        self.REMOVED = '<removed-task>'      # placeholder for a removed task
        self.counter = itertools.count()     # unique sequence count

    def add_task(self, task, priority=0):
        'Add a new task or update the priority of an existing task'
        if task in self.entry_finder:
            self.remove_task(task)
        count = next(self.counter)
        entry = [priority, count, task]
        self.entry_finder[task] = entry
        heappush(self.pq, entry)

    def remove_task(self, task):
        'Mark an existing task as REMOVED.  Raise KeyError if not found.'
        entry = self.entry_finder.pop(task)
        entry[-1] = self.REMOVED

    def pop_task(self):
        'Remove and return the lowest priority task. Raise KeyError if empty.'
        while self.pq:
            priority, count, task = heappop(self.pq)
            if task is not self.REMOVED:
                del self.entry_finder[task]
                return task
        raise KeyError('pop from an empty priority queue')

    def top_task(self):
        return self.pq[0][-1]

    def get_length(self):
        return len(self.pq)

    def is_empty(self):
        return self.get_length()==0


class Sssp(object):
    """ single source shortest path """
    INFINITY = 999999999
    class Status(Enum):
        """ ?????????????¨??????¶??? """
        white = 1  # ????¨????
        gray = 2  # ?¨???????
        black = 3  #?¨???????

    def __init__(self, data):
        num_of_nodes = len(data)
        self.color = [Sssp.Status.white] * num_of_nodes  # ????????????????¨??????¶???
        self.d = [Sssp.INFINITY] * num_of_nodes  # ?§???????????????????
        self.p = [-1] * num_of_nodes  # ????????????????????????????¨?????????????????????????
        self.adj = [[] for _ in range(num_of_nodes)]
        self.make_adj(data)


    def make_adj(self, data):
        # ??£??\??????????????????
        for d in data:
            my_id = d[0]
            d = d[2:]
            while len(d) > 1:
                to_node, cost = d[0], d[1]
                self.adj[my_id].insert(0, (to_node, cost))
                d = d[2:]


    def dijkstra(self, start):
        self.d[start] = 0
        pq = Priority_queue()
        pq.add_task((0, 0), 0)

        while not pq.is_empty():
            cost, u = pq.pop_task()
            self.color[u] = Sssp.Status.black
            if self.d[u] < cost:
                continue

            for v, cost in self.adj[u]:
                if self.color[v] == Sssp.Status.black:
                    continue
                if self.d[v] > self.d[u] + cost:
                    self.d[v] = self.d[u] + cost
                    pq.add_task((self.d[v], v), self.d[v])
                    self.color[v] = Sssp.Status.gray


if __name__ == '__main__':
    # ??????????????\???
    num = int(input())
    data = []
    for i in range(num):
        data.append(list(map(int, input().split(' '))))

    # ???????????¢?´¢
    s = Sssp(data)
    s.dijkstra(0)

    # ???????????¨???
    for i in range(num):
        print('{0} {1}'.format(i, s.d[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 heapq as hp
import sys

WHITE, GRAY, BLACK = 0, 1, 2


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

    def __init__(self, in_data):
        v_num = int(in_data[0])
        vertices = map(lambda x: x.split(), in_data[1:])
        adj_table = [{} for _ in range(v_num)]

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

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

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

            for adj, cost in self.adj_table[current].items():
                if self.visited[adj] != 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 = SingleSourceShortestPath(in_data=_input)
    ans = case.path_heap_dijkstra(root=0)
    for i, v in enumerate(ans):
        print(i, v)