如何删除从结构numpy的数组*列，而不将其复制*？ [英] How to remove a column from a structured numpy array *without copying it*?

问题描述

由于结构化numpy的数组，我想删除的名字一定不列复制阵列。
我知道我可以做到这一点：

Given a structured numpy array, I want to remove certain columns by name without copying the array. I know I can do this:

names = list(a.dtype.names)
if name_to_remove in names:
    names.remove(name_to_remove)
a = a[names]

但这个创建数组的临时副本，我想避免，因为我处理数组可能是非常大的。

But this creates a temporary copy of the array which I want to avoid because the array I am dealing with might be very large.

有没有做到这一点的好办法？

Is there a good way to do this?

推荐答案

您可以创建一个只包含所需的字段创建新的数据类型，具有相同的字段偏移和相同的itemsize和原阵列的数据类型，然后使用这种新的数据类型来创建原始数组的一个观点。在 DTYPE 函数处理与多种格式的参数;有关的一个在文档的一节<一个描述href=\"http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html#specifying-and-constructing-data-types\"相对=nofollow>指定，构建数据类型。向下滚动到开头的第

You can create a new data type containing just the fields that you want, with the same field offsets and the same itemsize as the original array's data type, and then use this new data type to create a view of the original array. The dtype function handles arguments with many formats; the relevant one is described in the section of the documentation called "Specifying and constructing data types". Scroll down to the subsection that begins with

{'names': ..., 'formats': ..., 'offsets': ..., 'titles': ..., 'itemsize': ...}

下面是使用这个想法一对夫妇的方便的功能。

Here are a couple convenience functions that use this idea.

import numpy as np


def view_fields(a, names):
    """
    `a` must be a numpy structured array.
    `names` is the collection of field names to keep.

    Returns a view of the array `a` (not a copy).
    """
    dt = a.dtype
    formats = [dt.fields[name][0] for name in names]
    offsets = [dt.fields[name][1] for name in names]
    itemsize = a.dtype.itemsize
    newdt = np.dtype(dict(names=names,
                          formats=formats,
                          offsets=offsets,
                          itemsize=itemsize))
    b = a.view(newdt)
    return b


def remove_fields(a, names):
    """
    `a` must be a numpy structured array.
    `names` is the collection of field names to remove.

    Returns a view of the array `a` (not a copy).
    """
    dt = a.dtype
    keep_names = [name for name in dt.names if name not in names]
    return view_fields(a, keep_names)

例如，

In [297]: a
Out[297]: 
array([(10.0, 13.5, 1248, -2), (20.0, 0.0, 0, 0), (30.0, 0.0, 0, 0),
       (40.0, 0.0, 0, 0), (50.0, 0.0, 0, 999)], 
      dtype=[('x', '<f8'), ('y', '<f8'), ('i', '<i8'), ('j', '<i8')])

In [298]: b = remove_fields(a, ['i', 'j'])

In [299]: b
Out[299]: 
array([(10.0, 13.5), (20.0, 0.0), (30.0, 0.0), (40.0, 0.0), (50.0, 0.0)], 
      dtype={'names':['x','y'], 'formats':['<f8','<f8'], 'offsets':[0,8], 'itemsize':32})

确认 B 是通过改变 A 的视图（不是副本） b [0] ['X'] ...

Verify that b is a view (not a copy) of a by changing b[0]['x']...

In [300]: b[0]['x'] = 3.14

和看到， A 也发生了变化：

and seeing that a is also changed:

In [301]: a[0]
Out[301]: (3.14, 13.5, 1248, -2)

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