語境
從numpy1.16 版開始，如果您訪問結構化陣列的多個欄位，dtype結果陣列的 將具有與原始陣列相同的專案大小，從而導致額外的“填充”：
與 1.15 相比，Numpy 1.16 的新行為導致在未索引欄位的位置有額外的“填充”位元組。您將需要更新任何依賴于具有“打包”布局的資料的代碼。
這可能會導致問題，例如，如果您想稍后將欄位添加到有問題的陣列中：
import numpy as np
import numpy.lib.recfunctions
a = np.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')]
) # some array stolen from here: https://stackoverflow.com/a/37081693/5472354
print(a.shape, a.dtype, a.dtype.names, a.dtype.descr)
# all good so far
b = a[['x', 'i']] # for further processing I only need certain fields
print(b.shape, b.dtype, b.dtype.names, b.dtype.descr)
# you will only notice the extra padding in the descr
# b = np.lib.recfunctions.repack_fields(b)
# workaround
# now when I add fields, this becomes an issue
c = np.empty(b.shape, dtype=b.dtype.descr [('c', 'i4')])
c[list(b.dtype.names)] = b
c['c'] = 1
print(c.dtype.names)
print(c['f1'])
# the void fields are filled with raw data and were given proper names
# that can be accessed
現在的解決方法是使用numpy.lib.recfunctions.repack_fields，它洗掉了填充，我將來會使用它，但對于我以前的代碼，我需要修復。（雖然可能有問題與recfunctions，因為模塊可能無法找到，作為對我的情況下，這樣的附加import numpy.lib.recfunctions。陳述句）
題
這部分代碼是我用來向陣列添加欄位的內容（基于此）：
c = np.empty(b.shape, dtype=b.dtype.descr [('c', 'i4')])
c[list(b.dtype.names)] = b
c['c'] = 1
雖然（現在我知道了）使用numpy.lib.recfunctions.require_fields可能更適合添加欄位。但是，我仍然需要一種方法來從b.dtype.descr以下位置洗掉空欄位：
[('x', '<f8'), ('', '|V8'), ('i', '<i8'), ('', '|V8')]
This is just a list of tuples, so I guess I could construct a more or less awkward way (along the lines of descr.remove(('', '|V8'))) to deal with this, but I was wondering if there is a better way, especially since the size of the voids depends on the number of left-out fields, e.g. from V8 to V16 if there are two in a row and so on (instead of a new void for each left-out field). So the code would become real clunky real fast.
uj5u.com熱心網友回復：
In [237]: a = np.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 [238]: a
Out[238]:
array([(10., 13.5, 1248, -2), (20., 0. , 0, 0),
(30., 0. , 0, 0), (40., 0. , 0, 0),
(50., 0. , 0, 999)],
dtype=[('x', '<f8'), ('y', '<f8'), ('i', '<i8'), ('j', '<i8')])
的b觀點：
In [240]: b = a[['x','i']]
In [241]: b
Out[241]:
array([(10., 1248), (20., 0), (30., 0), (40., 0), (50., 0)],
dtype={'names':['x','i'], 'formats':['<f8','<i8'], 'offsets':[0,16], 'itemsize':32})
重新打包的副本：
In [243]: c = rf.repack_fields(b)
In [244]: c
Out[244]:
array([(10., 1248), (20., 0), (30., 0), (40., 0), (50., 0)],
dtype=[('x', '<f8'), ('i', '<i8')])
In [245]: c.dtype
Out[245]: dtype([('x', '<f8'), ('i', '<i8')])
您在添加欄位時過度填充的嘗試：
In [247]: d = np.empty(b.shape, dtype=b.dtype.descr [('c', 'i4')])
...: d[list(b.dtype.names)] = b
...: d['c'] = 1
In [248]: d
Out[248]:
array([(10., b'\x00\x00\x00\x00\x00\x00\x00\x00', 1248, b'\x00\x00\x00\x00\x00\x00\x00\x00', 1),
(20., b'\x00\x00\x00\x00\x00\x00\x00\x00', 0, b'\x00\x00\x00\x00\x00\x00\x00\x00', 1),
...],
dtype=[('x', '<f8'), ('f1', 'V8'), ('i', '<i8'), ('f3', 'V8'), ('c', '<i4')])
我第一次嘗試制作不包含Void欄位的 dtype 。我不知道簡單地測驗V是否足夠健壯：
In [253]: [des for des in b.dtype.descr if not 'V' in des[1]]
Out[253]: [('x', '<f8'), ('i', '<i8')]
并從中創建一個新的資料型別：
In [254]: d_dtype = _ [('c','i4')]
所有這些都是正常的 Python 串列和元組操作。我在其他recfunctions. 我懷疑repack_fields做了這樣的事情。
現在我們用更簡單的 dtype 創建一個新陣列：
In [255]: d = np.empty(b.shape, dtype=d_dtype)
In [256]: d[list(b.dtype.names)] = b
...: d['c'] = 1
In [257]: d
Out[257]:
array([(10., 1248, 1), (20., 0, 1), (30., 0, 1), (40., 0, 1),
(50., 0, 1)], dtype=[('x', '<f8'), ('i', '<i8'), ('c', '<i4')])
我已經從repack_fields構造一個新的、未填充的 dtype 的代碼中提取出來：
In [262]: def foo(a):
...: fieldinfo = []
...: for name in a.names:
...: tup = a.fields[name]
...: fmt = tup[0]
...: if len(tup) == 3:
...: name = (tup[2], name)
...: fieldinfo.append((name, fmt))
...: print(fieldinfo)
...: dt = np.dtype(fieldinfo)
...: return dt
...:
...:
In [263]: foo(b.dtype)
[('x', dtype('float64')), ('i', dtype('int64'))]
Out[263]: dtype([('x', '<f8'), ('i', '<i8')])
這適用于dtype.fields而不是dtype.descr. 一個是dict另一個串列。
In [274]: b.dtype
Out[274]: dtype({'names':['x','i'], 'formats':['<f8','<i8'], 'offsets':[0,16], 'itemsize':32})
In [275]: b.dtype.descr
Out[275]: [('x', '<f8'), ('', '|V8'), ('i', '<i8'), ('', '|V8')]
In [276]: b.dtype.fields
Out[276]: mappingproxy({'x': (dtype('float64'), 0), 'i': (dtype('int64'), 16)})
In [277]: b.dtype.fields['x']
Out[277]: (dtype('float64'), 0)
descr從b.dtype以下位置獲取有效元組的另一種方法：
In [278]: [des for des in b.dtype.descr if des[0] in b.dtype.names]
Out[278]: [('x', '<f8'), ('i', '<i8')]
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