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: class RadixHeap:
    __slots__ = ["data", "last", "siz", "used"]

    # (max_key - min_key) <= C
    def __init__(self, N, C):
        self.data = [[] for i in range(C.bit_length() + 1)]
        self.last = self.siz = 0
        self.used = [0]*N

    def push(self, x, key):
        #assert self.last <= x
        self.siz += 1
        self.data[(x ^ self.last).bit_length()].append((x, key))

    def pop(self):
        data = self.data
        used = self.used
        #assert self.siz > 0
        if not data[0]:
            i = 1
            while not data[i]:
                i += 1
            d = data[i]
            new_last, new_key = min(d)
            used[new_key] = 1
            for val in d:
                x, key = val
                if used[key]:
                    self.siz -= 1
                    continue
                data[(x ^ new_last).bit_length()].append(val)
            self.last = new_last
            data[i] = []
        else:
            new_last, new_key = data[0].pop()
            used[new_key] = 1
            self.siz -= 1
        return new_last, new_key

    def __len__(self):
        return self.siz

import sys
readline = sys.stdin.readline
write = sys.stdout.write


N = int(readline())
G = [None] * N
for i in range(N):
    u, k, *V = map(int, readline().split())
    G[i] = [(v, c) for v, c in zip(V[::2], V[1::2])]

def dijkstra(N, G, s):
    que = RadixHeap(N, 10**9 + 1)

    dist = [10**18]*N
    dist[s] = 0
    que.push(0, s)
    while que:
        cost, v = que.pop()
        if dist[v] < cost:
            continue
        for w, c in G[v]:
            if cost + c < dist[w]:
                dist[w] = r = cost + c
                que.push(r, w)
    return dist
dist = dijkstra(N, G, 0)
write("\n".join("%d %d" % (i, d) for i, d in enumerate(dist)))
write("\n")

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_node, min_dist = heappop(h)
        if nodes[min_node].dist < min_dist:
            continue

        nodes[min_node].color = Node.BLACK

        for adj, c in nodes[min_node].adjs:
            if nodes[min_node].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, (adj, nodes[adj].dist))


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: n = int(input())
from collections import defaultdict
G = defaultdict(list)
for i in range(n):
    i = [int(i) for i in input().split()]
    k = i[1]
    G[i[0]] = list(zip(i[2::2],i[3::2]))

def mind(Q, d):
    c = float('inf')
    ret = 0
    for i, j in enumerate(d):
        if c > j and i in Q:
            c = j
            ret = i
    return ret

def dijkstra(G):
    d = [0] + [float('inf')] * (n - 1)
    Q = set(range(n))
    while len(Q) != 0:
        u = mind(Q, d)
        Q = Q - set([u])
        for v, c in G[u]:
            if d[v] > d[u] + c:
                d[v] = d[u] + c
    return d

for i,j in enumerate(dijkstra(G)):
    print(i, j)