Go: package main

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

const NIL = -1

type Node struct {
	id     int
	weight int
}

func init() {
	sc.Split(bufio.ScanWords)
}

type NodeHeap []Node

func (h NodeHeap) Len() int           { return len(h) }
func (h NodeHeap) Less(i, j int) bool { return h[i].weight < h[j].weight }
func (h NodeHeap) Swap(i, j int)      { h[i], h[j] = h[j], h[i] }

func (h *NodeHeap) Push(x interface{}) {
	*h = append(*h, x.(Node))
}

func (h *NodeHeap) Pop() interface{} {
	old := *h
	n := len(old)
	x := old[n-1]
	*h = old[0 : n-1]
	return x
}

var sc = bufio.NewScanner(os.Stdin)

func scanInt() int {
	num, _ := strconv.Atoi(scanString())
	return num
}

func scanString() string {
	sc.Scan()
	return sc.Text()
}

func init() {
	sc.Split(bufio.ScanWords)
}

func main() {
	n := scanInt()

	list := make([][]int, n)
	weight := make([]int, n)
	for i := range list {
		list[i] = make([]int, n)
		weight[i] = NIL
		for j := range list[i] {
			list[i][j] = NIL
		}
	}

	for i := 0; i < n; i++ {
		u := scanInt()
		k := scanInt()
		for j := 0; j < k; j++ {
			v := scanInt()
			w := scanInt()
			list[u][v] = w
		}
	}

	start := Node{id: 0, weight: 0}
	h := &NodeHeap{start}
	heap.Init(h)

	for h.Len() > 0 {
		node := heap.Pop(h).(Node)
		if weight[node.id] != NIL {
			continue
		}
		weight[node.id] = node.weight

		for i, w := range list[node.id] {
			if w == NIL {
				continue
			}
			heap.Push(h, Node{id: i, weight: w + node.weight})
		}
	}

	w := bufio.NewWriter(os.Stdout)
	for i := 0; i < n; i++ {
		fmt.Fprint(w, i)
		fmt.Fprint(w, " ")
		fmt.Fprint(w, weight[i])
		fmt.Fprintln(w)
	}
	w.Flush()
}

Python: # -*- coding: utf-8 -*-
from heapq import heappop, heappush


def dijkstra(edges, start, *, adj_matrix=False, default_value=float('inf')):
    '''
    Returns best costs to each node from 'start' node in the given graph.
    (Single Source Shortest Path - SSSP)

    If edges only include costs and destination nodes on possible edges,
    adj_matrix should be False(default), and it is generally when this functions works much faster

    Note that costs in edges should follow the rules below
    1. The cost from a node itself should be 0,
    2. If there is no edge between nodes, the cost between them should be default_value
    3. The costs can't be negative

    # sample input when given as adjacency matrix (adj_matrix=True)
    edges = [[0, 2, 5, 4, inf, inf],           # node 0
             [2, 0, inf, 3, 6, inf],           # node 1
             [5, inf, 0, 2, inf, 6],           # node 2
             [4, 3, 2, 0, 2, inf],             # node 3
             [inf, 6, inf, 2, 0, 4],           # node 4
             [inf, inf, 6, inf, 4, 0]]         # node 5

    # sample input when given not as adjacency matrix (adj_matrix=False)
    edges = [[(1, 2), (2, 5), (3, 4)],         # node 0
             [(0, 2), (3, 3), (4, 6)],         # node 1
             [(0, 5), (3, 2), (5, 6)],         # node 2
             [(0, 4), (1, 3), (2, 2), (4, 2)], # node 3
             [(1, 6), (3, 2), (5, 4)],         # node 4
             [(2, 6), (4, 4)]]                 # node 5
    '''

    n = len(edges)

    inf = float('inf')
    costs = [inf] * n
    costs[start] = 0
    pq, rem = [(0, start)], n - 1

    while pq and rem:
        tmp_cost, tmp_node = heappop(pq)
        if costs[tmp_node] < tmp_cost:
            continue
        rem -= 1

        nxt_edges = ((node, cost) for node, cost in enumerate(edges[tmp_node]) if cost != default_value) if adj_matrix else\
                    edges[tmp_node]

        for nxt_node, nxt_cost in nxt_edges:
            new_cost = tmp_cost + nxt_cost
            if costs[nxt_node] > new_cost:
                costs[nxt_node] = new_cost
                heappush(pq, (new_cost, nxt_node))

    return costs


def dijkstra_route(edges,
                   start,
                   goal,
                   *,
                   adj_matrix=False,
                   default_value=float('inf'),
                   verbose=False):
    '''
    Trys to find the best route to the 'goal' from the 'start'
    '''

    n = len(edges)

    inf = float('inf')
    costs = [inf] * n
    costs[start] = 0
    pq, rem = [(0, start)], n - 1
    prevs = [-1 for _ in [None] * n]

    while pq and rem:
        tmp_cost, tmp_node = heappop(pq)
        if costs[tmp_node] < tmp_cost:
            continue
        rem -= 1

        nxt_edges = ((node, cost) for node, cost in enumerate(edges[tmp_node]) if cost != default_value) if adj_matrix else\
                    edges[tmp_node]

        for nxt_node, nxt_cost in nxt_edges:
            new_cost = tmp_cost + nxt_cost
            if costs[nxt_node] > new_cost:
                costs[nxt_node] = new_cost
                heappush(pq, (new_cost, nxt_node))
                prevs[nxt_node] = tmp_node

    min_route = []
    prev = goal
    cnt = 0
    while prev != start:
        min_route.append(prev)
        prev = prevs[prev]

        cnt += 1
        if prev == -1 or cnt > n:
            raise NoRouteError(
                'There is no possible route in this graph \nedges: {} \nstart: {} \ngoal: {}'.
                format(edges, start, goal))

    else:
        min_route.append(prev)
    min_route = min_route[::-1]
    min_cost = costs[goal]

    if verbose:
        print('---route---')
        for node in min_route[:-1]:
            print('{} -> '.format(node), end='')
        else:
            print(min_route[-1])

        print('---distance---')
        print(min_cost)

    return costs, min_route


class NoRouteError(Exception):
    pass


if __name__ == '__main__':
    '''
    an example of using this function
    AIZU ONLINE JUDGE - ALDS_1_12_B
    '''

    n = int(input())
    edges = [[] for i in range(n)]
    for i in range(n):
        i, k, *kedges = map(int, input().split())
        for edge in zip(kedges[::2], kedges[1::2]):
            edges[i].append(edge)

    for i, cost in enumerate(dijkstra(edges, 0)):
        print(i, cost)

Python: INF = 1e9

n = int(input())
adj_matrix = [[INF for i in range(n)] for j in range(n)]

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

def dijkstra(x):
    isVisited = [False] * x
    d = [INF] * x 
    d[0] = 0
    p = [0] * x 
    
    while True:
        min_cost = INF
        
        for i in range(x):
            if isVisited[i] != True and d[i] < min_cost:
                min_cost = d[i]
                u = i
        
        if min_cost == INF:
            break
        
        isVisited[u] = True
        
        for v in range(x):
            if isVisited[v] != True and adj_matrix[u][v] != INF:
                if d[u] + adj_matrix[u][v] < d[v]:
                    d[v] = d[u] + adj_matrix[u][v]
                    p[v] = u
    return d

for i, di in enumerate(dijkstra(n)):
    print(i, di)

Python: def dijkstra(matrix, s):
    INF = 200000
    n = len(matrix)
    state, d, p = [False] * n, [INF] * n, [-1] * n
    d[s] = 0
    while True:
        mincost = INF
        for i in range(n):
            if not state[i] and d[i] < mincost:
                mincost = d[i]
                u = i
        if mincost == INF:
            break
        state[u] = True
        for v in range(n):
            if not state[v] and matrix[u][v] != -1:
                if d[u] + matrix[u][v] < d[v]:
                    d[v] = d[u] + matrix[u][v]
                    p[v] = u
    return d


def main():
    n = int(input())
    m = [[-1] * n for _ in range(n)]
    for _ in range(n):
        u, k, *vc = map(int, input().split())
        for v, c in zip(vc[::2], vc[1::2]):
            m[u][v] = c
    dist = dijkstra(m, 0)
    for i, d in enumerate(dist):
        print(i, d)


if __name__ == '__main__':
    main()