Question

这实际上是两个问题。

我有一份年龄间隔列表。对于每个间隔，存在相应的值。区间和值按元组age_value_intervals列表组织（请参阅代码中的注释）。

我还有一个单独的不同年龄列表ages，我想知道它的价值。

以下代码是尝试将值映射到给定年龄。

现在回答问题，

为了为value_map分配值，我使用ages对value_map和zip进行迭代。然后我尝试分配给value。这不起作用。为什么呢？
我怀疑我使用的方法是非常有效的（如果它有效）。有没有更好的方法来实现这种映射？

import numpy as np

# List of tuples defining and age interval and the corresponing value for
# that interval. For instance (20, 30, 10) indicates that the age interval from
# 20 to 30 has the value 10
age_value_intervals = [(20, 30, 10),
                       (30, 35, 5),
                       (35, 42, 50),
                       (50, 56, 40),
                       (56, 60, 30)]

# The ages for which I would like to know the value
ages = [25, 30, 35, 40, 45, 50]

# Empty array used to stor the values for the corresponding age
value_map = np.empty(len(ages))
# I want the value to be nan if there is no known value
value_map[:] = np.nan

# Iterate over the ages I want to know the value for
for age, value in zip(ages, value_map):
    # Check if the age is in an interval for which the value is known
    for from_age, to_age, actual_value in age_value_intervals:
        if age >= from_age and age < to_age:
            # Assign the value to the value_map
            # This is were it falls apart (I guess...)
            value = actual_value
            # Move on to the next age since we got a match
            break

#Expected output
value_map = [10, 5, 50, 50, nan, 40]

Answer 1

我建议您将numpy.digitize与dict一起使用。当值无法映射到范围时，您可以手动考虑实例。

import numpy as np

age_value_intervals = [(20, 30, 10),
                       (30, 35, 5),
                       (35, 42, 50),
                       (50, 56, 40),
                       (56, 60, 30)]

ages = np.array([25, 30, 35, 40, 45, 50])

bins = np.array([x[0] for x in age_value_intervals])
mapper = dict(enumerate([x[2] for x in age_value_intervals], 1))    

res = np.array([mapper[x] for x in np.digitize(ages, bins)], dtype=float)

for idx in range(len(ages)):
    if not any(i <= ages[idx] <= j for i, j, k in age_value_intervals):
        res[idx] = np.nan

结果：

array([ 10.,   5.,  50.,  50.,  nan,  40.])

Answer 2

首先，如评论中所述，如果您尝试分配给变量，您当前正在更改内部循环，那么该值就会丢失。

其次，大多数映射都是多余的。

这样的事情可能仍有待改进但应该有效：

result=[] 
for check_age in ages:
    for from_age, to_age, value in age_value_intervals:
        if check_age in range(from_age, to_age):
            result+=[value]

print result

注意，如果您需要在间隔中不时添加一些结果，则需要额外的代码。

分配for循环值

2 个答案: