python - 计算平均值时如何从列表中排除 0 值

Question

我有一个这样的成绩列表：

grades = [[[4.0, 3.0], [3.0, 3.0], [4.0, 3.0], [3.33, 3.33], [3.0, 3.0], [4.0, 3.67], [3.0, 3.67], [4.0, 3.0], [4.0, 3.33], [4.0, 3.33], [3.0, 3.0], [3.67, 3.67], [3.33, 3.0], [4.0, 3.0], [3.0, 3.67], [3.33, 4.0], [4.0, 3.33], [4.0, 4.0], [3.0, 3.67], [3.0, 3.67], [3.67, 3.33], [4.0, 3.0], [3.0, 4.0], [3.67, 3.67], [4.0, 3.33], [2.33, 2.0], [3.0, 2.67], [2.67, 3.67], [2.33, 2.0]], [[3.0, 3.67], [3.33, 4.0]], [[2.33, 4.0]], [[4.0, 0], [3.67, 0], [3.67, 0], [4.0, 0], [3.0, 0], [3.67, 0], [3.33, 0], [3.0, 0], [3.67, 3.67], [3.0, 0], [4.0, 0], [4.0, 0], [3.0, 0], [3.67, 0], [3.0, 0], [4.0, 0], [3.0, 0], [3.33, 0], [4.0, 0], [4.0, 0], [3.33, 0], [0, 3.33], [3.33, 0], [4.0, 3.0], [4.0, 0], [4.0, 0]], [[3.0, 3.67], [3.67, 3.67], [4.0, 3.67], [3.0, 3.0], [3.0, 3.0], [3.67, 4.0], [4.0, 3.0], [3.33, 4.0], [3.67, 3.33], [3.0, 3.67], [3.67, 3.0], [3.0, 3.0], [3.0, 3.33], [3.33, 3.0], [4.0, 3.67], [3.33, 4.0], [4.0, 3.33], [3.67, 3.0], [3.67, 4.0], [3.0, 3.33], [3.0, 3.0], [3.67, 4.0], [3.67, 3.0], [3.33, 3.67], [3.0, 3.33], [3.33, 4.0], [3.0, 4.0], [3.0, 4.0], [3.33, 3.33], [3.33, 3.33], [3.67, 3.33], [4.0, 3.67], [3.33, 3.33], [4.0, 3.67], [3.33, 3.67], [4.0, 3.33], [3.67, 3.0], [3.33, 3.0], [3.67, 3.33], [3.0, 4.0], [3.67, 4.0], [3.67, 3.0], [3.67, 3.33], [3.33, 3.33], [3.67, 3.0], [4.0, 4.0], [3.0, 3.67], [3.0, 3.0], [3.0, 3.0], [3.33, 4.0], [3.67, 4.0], [3.0, 4.0], [3.67, 3.67], [3.0, 3.33], [3.67, 4.0], [3.33, 3.33], [4.0, 4.0], [3.33, 4.0], [3.33, 4.0], [3.0, 3.67], [4.0, 3.33], [3.33, 4.0], [3.0, 3.0], [3.33, 3.67], [3.33, 4.0], [3.67, 3.33], [3.67, 3.33], [4.0, 3.67], [3.67, 4.0], [3.67, 3.0], [3.33, 3.33], [3.0, 3.67], [3.67, 3.33], [3.33, 3.0], [3.33, 4.0], [3.0, 3.0], [3.33, 4.0], [3.33, 4.0], [4.0, 3.67], [4.0, 3.0], [3.67, 4.0], [3.33, 3.0], [3.67, 3.67], [3.67, 3.0], [3.33, 3.0], [4.0, 4.0], [3.33, 4.0], [3.0, 3.33], [3.67, 3.0], [4.0, 3.33], [3.67, 4.0], [3.0, 2.33], [2.0, 3.33], [2.0, 3.67], [2.0, 2.33], [3.67, 3.33], [2.0, 2.33], [3.67, 2.33], [2.33, 3.67], [3.67, 3.67], [3.33, 2.67], [3.33, 2.0], [3.67, 4.0], [2.0, 4.0], [4.0, 2.0], [3.0, 2.0], [2.33, 2.67], [4.0, 4.0], [3.0, 2.33], [3.0, 3.33], [2.33, 3.0], [4.0, 3.33], [3.67, 4.0], [2.0, 2.0], [2.33, 4.0], [4.0, 2.0], [2.67, 3.33], [2.0, 3.67], [3.33, 4.0]], [[3.0, 0], [3.33, 0], [3.67, 0], [3.67, 0], [3.67, 0], [3.33, 0], [3.0, 0], [3.0, 0], [3.33, 0], [3.0, 0], [4.0, 0], [3.0, 0], [3.0, 0], [3.67, 0], [3.67, 3.67], [3.67, 0], [3.67, 0], [4.0, 0], [3.67, 0], [3.0, 0], [3.0, 0], [4.0, 0], [3.0, 0], [3.0, 0], [3.0, 0], [3.33, 0], [3.0, 0], [3.67, 0], [4.0, 0], [3.0, 0], [3.0, 0], [3.0, 0], [3.33, 0], [4.0, 0], [4.0, 0], [3.67, 0], [4.0, 0], [3.33, 0], [3.0, 0], [3.67, 0], [3.67, 0], [4.0, 0], [3.33, 0], [3.33, 0], [4.0, 0], [3.33, 0], [3.33, 0], [3.0, 0], [3.0, 0], [3.67, 0], [4.0, 0], [0, 3.0], [3.33, 0], [4.0, 0], [3.67, 0], [4.0, 0], [0, 4.0], [0, 3.33], [0, 3.67], [0, 3.0], [0, 4.0], [0, 3.0], [0, 3.67], [0, 3.0], [0, 3.0], [4.0, 0], [0, 3.67], [0, 3.0], [0, 3.0], [0, 4.0], [3.0, 0], [0, 3.0], [0, 3.67], [0, 3.0], [0, 3.33], [0, 4.0], [3.33, 0], [3.0, 3.67], [3.67, 3.0], [3.0, 3.0], [4.0, 4.0], [3.67, 3.0], [3.0, 3.0], [3.67, 3.0], [3.0, 3.0], [3.67, 4.0], [3.33, 3.67], [3.33, 0], [4.0, 0], [3.67, 0], [3.67, 0], [4.0, 0], [3.33, 0], [4.0, 0], [4.0, 0], [3.0, 0], [3.67, 0], [3.0, 0], [4.0, 0], [4.0, 0], [3.0, 0], [3.33, 0], [3.33, 0], [3.33, 0], [3.0, 0], [3.67, 0], [4.0, 0], [3.0, 0], [3.67, 0], [3.67, 0], [3.67, 0], [3.67, 0], [3.0, 0], [3.33, 0], [4.0, 0], [3.0, 0], [3.33, 0], [3.33, 0], [4.0, 0], [4.0, 0], [4.0, 0], [4.0, 0], [3.0, 0], [3.67, 0], [3.67, 0], [3.33, 0], [4.0, 0], [4.0, 0], [3.0, 0], [3.0, 0], [4.0, 0], [3.0, 0], [3.33, 0], [3.33, 0], [3.0, 0], [3.67, 0], [3.67, 0], [3.0, 0], [3.0, 0], [3.67, 0], [3.0, 0], [3.33, 0], [4.0, 0], [4.0, 0], [3.67, 0], [3.33, 0], [3.0, 0], [3.67, 0], [2.33, 0], [0, 2.33]], [[3.67, 3.33], [4.0, 3.67], [3.0, 3.33], [3.33, 3.67], [3.0, 3.33], [3.0, 3.33], [3.0, 3.33], [4.0, 3.33], [3.33, 3.67], [3.0, 3.67], [3.0, 3.0], [3.33, 3.0], [3.0, 3.67], [4.0, 3.33], [3.33, 4.0], [3.0, 3.33], [4.0, 4.0], [3.67, 3.67], [4.0, 3.33], [3.0, 4.0], [3.0, 3.67], [4.0, 3.0], [3.67, 3.67], [4.0, 3.33], [3.0, 3.33], [3.0, 4.0], [3.0, 3.33], [3.33, 3.33], [3.67, 3.0], [3.33, 3.33], [3.33, 3.0], [3.33, 3.33], [3.0, 3.67], [3.0, 3.67], [4.0, 3.33], [3.0, 4.0], [4.0, 3.67], [3.0, 3.0], [3.67, 4.0], [4.0, 3.67], [3.0, 3.67], [2.33, 3.67], [3.67, 3.67], [3.0, 2.33], [3.67, 2.33], [3.0, 2.33], [3.67, 4.0], [2.33, 2.33], [3.0, 3.67], [4.0, 2.67], [4.0, 3.67], [2.0, 2.0], [4.0, 2.67], [2.0, 3.33], [3.0, 2.33], [2.0, 3.0], [3.67, 2.33], [3.67, 2.67], [3.33, 2.33], [2.67, 2.0], [4.0, 3.67], [4.0, 4.0], [2.67, 4.0], [4.0, 3.33], [2.67, 2.33], [2.0, 4.0], [3.67, 2.33], [3.0, 2.67], [2.67, 3.0], [2.67, 4.0], [3.67, 2.33], [2.0, 3.33], [2.0, 3.0], [3.0, 4.0], [3.33, 3.0]], [[3.67, 4.0], [4.0, 4.0], [4.0, 4.0], [3.67, 3.67], [4.0, 4.0], [3.0, 3.67], [3.67, 3.33], [3.0, 4.0], [3.0, 4.0], [4.0, 3.0], [3.33, 3.67], [3.33, 4.0], [3.33, 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我想找到每个子列表的平均值。出于这个原因，我这样做了：

gpa = [[mean(sub_list) for sub_list in list] for list in grades]

但问题是某些子列表的值为 0.0，当然代码会考虑它。有没有办法让我在它们出现时忽略那些 0.0 并根据其余值计算平均值？根据这个列表，我每个子列表只有 2 个值，但还有其他包含 4 个和 5 个元素的值。这对于我正在计算的事情至关重要，这就是为什么我根本不需要考虑那些 0。

我正在使用统计平均值

标签： pythonliststatisticsmean

Answer 1

首先，您的平均值不会受到零值的影响。

其次，如果您忽略零，那么您是否会减少均值公式中的除数，即（mean = sum(x) / (n - no_of_zeros)。

如果您需要第二件事，那么使用your_list.count(0)并从 list 的长度中减去它 ie len(your_list)。

这样，您就可以真正忽略零。