每个元素都是数组时填充新列表 [英] Populating new list when each element is an array

问题描述

我正在尝试根据一些现有数据创建一个新的数据列表。目前，尽管我遇到了一个问题，其中我的新列表的每个元素都被另存为自己的单元素列表。我不明白为什么会这样。我的代码当前类似于：

I'm trying to create a new list of data from some existing data. Currently though I am having the problem where each element of my new list is being saved as its own single-element list. I can't see why this is. My code is currently something like:

old_data = np.reshape(old_data, (49210, 1)) #Reshape into 1D array
new_data = [None] #Create empty list to be filled
max_value = np.nanmax(old_data)
threshold = 0.75 * max_value #Create threshold to be used as condition for new list.

for x in range(len(old_data)):
    if old_data[x] > threshold:
        new_data.append(old_data[x])

请注意， old_data 存储为2D数组，这就是为什么我在开始时就包含了 np.reshape 命令的原因。

Please note that old_data was stored as a 2D array, which is why I have included that np.reshape command at the start.

如上所述，每次满足条件时，该值都会作为新元素存储在 new_data 中，但是该元素是一个float32类型的数组，其大小为（ 1）。因此 new_data 最终是一个数组数组。我不确定为什么会这样。我真的很喜欢从此输出常规数组/列表，以便new_data易于处理。

As I said above, each time the condition is fulfilled the value is stored as a new element in new_data, but the element is an array of type float32 with size (1,). So new_data has ended up being an array of arrays. I'm not sure why this is. I would really just like a regular array/list output from this so that new_data is tractable. Any advice is appreciated.

推荐答案

我没有数据，但这可能有用：

I do not have the data but this might work:

old_data = np.reshape(old_data, (49210, 1)) #Reshape into 1D array
threshold = np.nanmax(old_data) #Create threshold to be used as condition for new 
new_data = [element for element in old_data if element > threshold]

列表理解不仅速度更快而且更漂亮，而不是使用带附加的for循环。

List comprehension is both faster and much prettier to use instead of for loops with append.

如果您遇到的是数组数组，则可能需要尝试以下操作：

In case you have ended up with a array of arrays you might want to try the following instead:

old_data = np.reshape(old_data, (49210, 1)) #Reshape into 1D array
threshold = np.nanmax(old_data) #Create threshold to be used as condition for new 
new_data = [element[0] for element in old_data if element[0] > threshold]

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