我们可以为List使用扩展方法，并使用线程安全的随机生成器组合。我在NuGet上打包了一个改进的版本，并在GitHub上提供源代码。NuGet版本包含可选的密码强随机。

Pre -。NET 6.0版本:

[MethodImpl(MethodImplOptions.AggressiveInlining)]
public static void Shuffle<T>(this IList<T> list)
{
    if (list == null) throw new ArgumentNullException(nameof(list));
    int n = list.Count;
    while (n > 1)
    {
        int k = ThreadSafeRandom.Instance.Next(n--);
        (list[n], list[k]) = (list[k], list[n]);
    }
}

internal class ThreadSafeRandom
{
    public static Random Instance => _local.Value;

    private static readonly Random _global = new Random();
    private static readonly ThreadLocal<Random> _local = new ThreadLocal<Random>(() =>
    {
        int seed;
        lock (_global)
        {
            seed = _global.Next();
        }
        return new Random(seed);
    });
}

在。net 6.0或更高版本上:

[MethodImpl(MethodImplOptions.AggressiveInlining)]
public static void Shuffle<T>(this IList<T> list)
{
    ArgumentNullException.ThrowIfNull(list);
    int n = list.Count;
    while (n > 1)
    {
        int k = Random.Shared.Next(n--);
        (list[n], list[k]) = (list[k], list[n]);
    }
}

通过NuGet安装库以获得更多功能。

如果您有一个固定的数字(75)，您可以创建一个包含75个元素的数组，然后枚举您的列表，将元素移动到数组中的随机位置。您可以使用Fisher-Yates shuffle生成列表号到数组索引的映射。

如果我们只需要以完全随机的顺序洗牌项目(只是在一个列表中混合项目)，我更喜欢这个简单而有效的代码，按guid排序项目…

var shuffledcards = cards.OrderBy(a => Guid.NewGuid()).ToList();

正如人们在评论中指出的那样，guid不能保证是随机的，所以我们应该使用真正的随机数生成器:

private static Random rng = new Random();
...
var shuffledcards = cards.OrderBy(a => rng.Next()).ToList();

I'm bit surprised by all the clunky versions of this simple algorithm here. Fisher-Yates (or Knuth shuffle) is bit tricky but very compact. Why is it tricky? Because your need to pay attention to whether your random number generator r(a,b) returns value where b is inclusive or exclusive. I've also edited Wikipedia description so people don't blindly follow pseudocode there and create hard to detect bugs. For .Net, Random.Next(a,b) returns number exclusive of b so without further ado, here's how it can be implemented in C#/.Net:

public static void Shuffle<T>(this IList<T> list, Random rnd)
{
    for(var i=list.Count; i > 0; i--)
        list.Swap(0, rnd.Next(0, i));
}

public static void Swap<T>(this IList<T> list, int i, int j)
{
    var temp = list[i];
    list[i] = list[j];
    list[j] = temp;
}

试试这段代码。

这里是Fisher-Yates shuffle的实现，允许指定返回的元素数量;因此，在获取所需数量的元素之前，没有必要首先对整个集合进行排序。

交换元素的顺序与默认值相反;从第一个元素到最后一个元素，因此检索集合的一个子集与洗牌整个集合产生相同的(部分)序列:

collection.TakeRandom(5).SequenceEqual(collection.Shuffle().Take(5)); // true

该算法基于Durstenfeld在维基百科上的(现代)Fisher-Yates shuffle。

public static IList<T> TakeRandom<T>(this IEnumerable<T> collection, int count, Random random) => shuffle(collection, count, random);
public static IList<T> Shuffle<T>(this IEnumerable<T> collection, Random random) => shuffle(collection, null, random);
private static IList<T> shuffle<T>(IEnumerable<T> collection, int? take, Random random)
{
    var a = collection.ToArray();
    var n = a.Length;
    if (take <= 0 || take > n) throw new ArgumentException("Invalid number of elements to return.");
    var end = take ?? n;
    for (int i = 0; i < end; i++)
    {
        var j = random.Next(i, n);
        (a[i], a[j]) = (a[j], a[i]);
    }

    if (take.HasValue) return new ArraySegment<T>(a, 0, take.Value);
    return a;
}

这是我最喜欢的shuffle方法，当不需要修改原始的时候。它是Fisher-Yates“由内到外”算法的变体，适用于任何可枚举序列(源的长度不需要从一开始就知道)。

public static IList<T> NextList<T>(this Random r, IEnumerable<T> source)
{
  var list = new List<T>();
  foreach (var item in source)
  {
    var i = r.Next(list.Count + 1);
    if (i == list.Count)
    {
      list.Add(item);
    }
    else
    {
      var temp = list[i];
      list[i] = item;
      list.Add(temp);
    }
  }
  return list;
}

该算法还可以通过分配一个从0到length - 1的范围来实现，并通过将随机选择的索引与最后一个索引交换来随机耗尽索引，直到所有索引都被选中一次。上面的代码完成了完全相同的事情，但没有额外的分配。非常简洁。

With regards to the Random class it's a general purpose number generator (and If I was running a lottery I'd consider using something different). It also relies on a time based seed value by default. A small alleviation of the problem is to seed the Random class with the RNGCryptoServiceProvider or you could use the RNGCryptoServiceProvider in a method similar to this (see below) to generate uniformly chosen random double floating point values but running a lottery pretty much requires understanding randomness and the nature of the randomness source.

var bytes = new byte[8];
_secureRng.GetBytes(bytes);
var v = BitConverter.ToUInt64(bytes, 0);
return (double)v / ((double)ulong.MaxValue + 1);

生成随机双精度(仅在0和1之间)的目的是用于扩展到整数解。如果你需要从一个基于随机双x的列表中选择一个东西，它总是0 <= x && x < 1是很简单的。

return list[(int)(x * list.Count)];

享受吧!