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 each in v_info:
        v_detail = map(int, each)
        v_index, v_adj_length, *v_adj_list = v_detail
        # 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]] = v_adj_list[j + 1]

    return init_adj_matrix


def dijkstra_path():
    # path search init
    start_vertex_index = 0
    path_list[start_vertex_index] = 0
    path_heap = []
    # heapq: rank by tuple[0], here ranked by d[u]
    hp.heappush(path_heap, (path_list[start_vertex_index], start_vertex_index))
    while len(path_heap) >= 1:
        current_vertex_index = hp.heappop(path_heap)[1]

        color[current_vertex_index] = BLACK

        for adj_vertex_index in range(vertices_num):
            current_adj_weight = adj_matrix[current_vertex_index].get(adj_vertex_index, -1)
            if current_adj_weight < 0:
                continue
            elif color[adj_vertex_index] is not BLACK:
                # d[u] + w[u,v]
                alt_path = path_list[current_vertex_index] + current_adj_weight
                if alt_path < path_list[adj_vertex_index]:
                    # update path_list
                    path_list[adj_vertex_index] = alt_path
                    # update heap
                    hp.heappush(path_heap, (alt_path, adj_vertex_index))
                    parent_list[adj_vertex_index] = current_vertex_index
                    color[adj_vertex_index] = 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_matrix = tuple(dict() for _ in range(vertices_num))
    adj_matrix = generate_adj_matrix(init_vertices_table)
    ans = dijkstra_path()
    for i, v in enumerate(ans):
        print(i, v)

Python: from sys import maxsize
from heapq import heappop, heappush


class Node:
    WHITE = 0
    GRAY = 1
    BLACK = 2

    def __init__(self):
        self.adjs = []
        self.color = Node.WHITE
        self.dist = maxsize

    def __lt__(self, other):
        return self.dist < other.dist

    def __repr__(self):
        return "Node({},{},{})".format(self.adjs, self.color, self.dist)


def read_adj():
    n = int(input())
    nodes = [Node() for _ in range(n)]

    for i in range(n):
        u, _, *rest = [int(x) for x in input().split()]
        for i in range(0, len(rest) - 1, 2):
            nodes[u].adjs.append((rest[i], rest[i+1]))

    return nodes


def shortest_path(nodes):
    h = []

    nodes[0].dist = 0
    nodes[0].color = Node.GRAY
    heappush(h, (0, 0))

    while len(h) > 0:
        min_dist, min_node = heappop(h)

        nodes[min_node].color = Node.BLACK
        if nodes[min_node].dist < min_dist:
            continue

        for adj, c in nodes[min_node].adjs:
            if nodes[adj].color == Node.BLACK:
                continue
            if nodes[min_node].dist + c < nodes[adj].dist:
                nodes[adj].dist = nodes[min_node].dist + c
                nodes[adj].color = Node.GRAY
                heappush(h, (nodes[adj].dist, adj))


def print_dist(nodes):
    for u, n in enumerate(nodes):
        print(u, n.dist)


def main():
    nodes = read_adj()
    shortest_path(nodes)
    print_dist(nodes)


if __name__ == '__main__':
    main()

Python: import sys, itertools
f = sys.stdin

def take2(iterable):
    i = iter(iterable)
    while True:
        yield next(i), next(i)

n = int(f.readline())
adj =[[(v, c) for v, c in take2(map(int, f.readline().split()[2:]))] for _ in range(n)]
d = [100000 * 100 + 1] * n

d[0] = 0
queue ={0}
while len(queue):
    parent = queue.pop()
    for v, c in adj[parent]:
        if d[v] > d[parent] + c:
            d[v] = d[parent] + c
            queue.union([v])

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

Python: n = int(input())
G = [[-1 for i in range(n)] for j in range(n)]
d = [999999 for i in range(n)]
  
for i in range(n):
    v = input().split()
    for j in range(int(v[1])):
        G[int(v[0])][int(v[2+2*j])] = int(v[3+2*j])

flag = [0] * n
tn = [0]
d[0] = 0
for k in range(n):
    for i in range(n):
        if i in tn:
            for j in range(n):
                if G[i][j] != -1:
                    if d[j] > d[i] + G[i][j]:
                        d[j] = d[i] + G[i][j]
                    if j not in tn:
                        tn.append(j)
  
for i in range(n):
    print(str(i) + " " + str(d[i]))