计算平均值时如何从列表中排除 0 值
How to exclude 0 values from a list when calculating the mean
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, 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2.0], [2.33, 3.33], [3.67, 2.0], [2.33, 3.0], [2.0, 2.67], [2.33, 4.0], [2.67, 3.0], [4.0, 3.67], [3.33, 3.67], [2.0, 4.0], [3.0, 2.0], [2.67, 4.0], [3.67, 2.67], [2.67, 2.67], [2.33, 3.67], [3.0, 2.0], [3.67, 4.0], [2.33, 4.0], [2.0, 3.67], [2.0, 4.0], [3.33, 4.0], [4.0, 2.67], [2.33, 3.33], [3.33, 3.33], [3.67, 2.33], [2.33, 3.33], [4.0, 2.33], [2.67, 2.67], [3.0, 3.33], [3.0, 3.33], [3.0, 4.0], [3.67, 3.67], [3.0, 3.67]], [[3.0, 3.67], [4.0, 4.0], [3.33, 3.33], [4.0, 3.0], [3.67, 3.33], [3.67, 3.67], [3.0, 3.0], [3.33, 3.67], [4.0, 3.0], [3.67, 4.0], [3.0, 4.0], [3.67, 3.67], [3.0, 3.67], [4.0, 3.33], [4.0, 4.0], [4.0, 3.67], [3.33, 4.0], [3.33, 3.67], [3.67, 4.0], [3.0, 3.67], [3.33, 4.0], [3.33, 4.0], [3.67, 3.33], [3.0, 4.0], [3.67, 3.67], [3.33, 3.0], [3.67, 4.0], [3.0, 3.67], [3.0, 3.33], [4.0, 4.0], [3.67, 4.0], [3.0, 3.0], [4.0, 3.67], [4.0, 3.0], [4.0, 3.0], [3.67, 4.0], [3.67, 3.33], [3.33, 3.0], [3.33, 3.0], [3.33, 3.0], [3.0, 3.67], [3.67, 3.33], [3.33, 3.67], [3.67, 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[3.0, 3.0], [3.0, 3.67], [3.33, 3.33], [4.0, 3.0], [3.33, 3.0], [4.0, 3.0], [4.0, 3.0], [4.0, 3.33], [3.67, 3.33], [3.33, 3.33], [3.33, 3.0], [2.33, 3.33], [2.67, 2.67], [3.33, 3.67], [2.0, 3.0], [3.67, 3.67], [3.0, 4.0], [3.33, 4.0], [4.0, 3.67], [2.67, 2.67], [4.0, 4.0], [3.33, 3.67], [2.67, 3.0], [3.33, 3.67], [2.67, 3.33], [3.33, 3.0], [4.0, 3.0], [2.0, 4.0], [4.0, 3.67], [2.0, 2.67], [4.0, 3.67], [2.0, 2.0], [3.67, 3.33], [4.0, 4.0], [2.0, 2.33], [3.67, 2.67], [2.33, 2.33], [3.0, 3.0], [3.33, 2.67], [4.0, 2.67], [2.0, 2.33], [3.0, 4.0], [3.67, 3.67], [3.33, 2.33], [2.67, 2.67], [2.67, 2.67], [2.0, 2.0], [3.67, 3.67], [3.0, 4.0], [2.67, 3.67], [3.0, 2.33]]]
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。有没有办法在它们出现时忽略那些 0.0 并根据其余值计算平均值？基于这个列表，我每个子列表只有 2 个值，但其他子列表中有 4 个和 5 个元素。这对我正在计算的事情至关重要，这就是为什么我需要根本不考虑那些 0 的原因。
我正在使用统计平均值
首先，你的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)。
如果你需要第二件事，那么，使用 your_list.count(0) 并从列表的长度中减去它，即 len(your_list).
这样，你真的可以忽略零了。
如果你可以使用 numpy，试试这个代码。
它简单快捷。
我将零替换为 np.nan，并使用 nanmean returns 除 np.nan.
之外的值的平均值
import numpy as np
grades = np.array(grades)
grades[grades == 0] = np.nan
np.nanmean(grades, axis = 1)
查看您的输入列表 grades，有些子列表的所有值都为零。如果您过滤所有零，mean 函数将抛出错误。
一种解决方案是在过滤掉所有值的情况下提供默认值零。
例如：
gpa = [[mean([i for i in sub_list if i!=0] or [0]) for sub_list in list] for list in grades]
print(gpa)
打印：
[[3.5, 3.0, 3.5, 3.33, 3.0, 3.835, 3.335, 3.5, 3.665, 3.665, 3.0, 3.67, 3.165, 3.5, 3.335, 3.665, 3.665, 4.0, 3.335, ...
... and so on (without throwing an error)
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], 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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。有没有办法在它们出现时忽略那些 0.0 并根据其余值计算平均值？基于这个列表，我每个子列表只有 2 个值，但其他子列表中有 4 个和 5 个元素。这对我正在计算的事情至关重要，这就是为什么我需要根本不考虑那些 0 的原因。
我正在使用统计平均值
首先，你的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)。
如果你需要第二件事，那么，使用 your_list.count(0) 并从列表的长度中减去它，即 len(your_list).
这样，你真的可以忽略零了。
如果你可以使用 numpy，试试这个代码。
它简单快捷。
我将零替换为 np.nan，并使用 nanmean returns 除 np.nan.
import numpy as np
grades = np.array(grades)
grades[grades == 0] = np.nan
np.nanmean(grades, axis = 1)
查看您的输入列表 grades，有些子列表的所有值都为零。如果您过滤所有零，mean 函数将抛出错误。
一种解决方案是在过滤掉所有值的情况下提供默认值零。
例如：
gpa = [[mean([i for i in sub_list if i!=0] or [0]) for sub_list in list] for list in grades]
print(gpa)
打印：
[[3.5, 3.0, 3.5, 3.33, 3.0, 3.835, 3.335, 3.5, 3.665, 3.665, 3.0, 3.67, 3.165, 3.5, 3.335, 3.665, 3.665, 4.0, 3.335, ...
... and so on (without throwing an error)