Python 如何仅展平 numpy 数组的某些维度
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How to flatten only some dimensions of a numpy array
提问by Curious
Is there a quick way to "sub-flatten" or flatten only some of the first dimensions in a numpy array?
有没有一种快速的方法来“亚展平”或仅展平 numpy 数组中的一些第一个维度？
For example, given a numpy array of dimensions (50,100,25), the resultant dimensions would be (5000,25)
例如，给定一个 numpy 数组的维度(50,100,25)，结果维度将是(5000,25)
采纳答案by Alexander
Take a look at numpy.reshape.
>>> arr = numpy.zeros((50,100,25))
>>> arr.shape
# (50, 100, 25)
>>> new_arr = arr.reshape(5000,25)
>>> new_arr.shape
# (5000, 25)
# One shape dimension can be -1.
# In this case, the value is inferred from
# the length of the array and remaining dimensions.
>>> another_arr = arr.reshape(-1, arr.shape[-1])
>>> another_arr.shape
# (5000, 25)
回答by Peter
A slight generalization to Alexander's answer - np.reshape can take -1 as an argument, meaning "total array size divided by product of all other listed dimensions":
对亚历山大的回答的一个小概括 - np.reshape 可以将 -1 作为参数，意思是“总数组大小除以所有其他列出维度的乘积”：
e.g. to flatten all but the last dimension:
例如压平除最后一个维度之外的所有维度：
>>> arr = numpy.zeros((50,100,25))
>>> new_arr = arr.reshape(-1, arr.shape[-1])
>>> new_arr.shape
# (5000, 25)
回答by KeithWM
A slight generalization to Peter's answer -- you can specify a range over the original array's shape if you want to go beyond three dimensional arrays.
对彼得回答的一个小概括——如果你想超越三维数组，你可以在原始数组的形状上指定一个范围。
e.g. to flatten all but the last twodimensions:
例如压平除最后两个维度之外的所有维度：
arr = numpy.zeros((3, 4, 5, 6))
new_arr = arr.reshape(-1, *arr.shape[-2:])
new_arr.shape
# (12, 5, 6)
EDIT: A slight generalization to my earlier answer -- you can, of course, also specify a range at the beginning of the of the reshape too:
编辑：对我之前的回答的一个小概括——当然，你也可以在重塑的开头指定一个范围：
arr = numpy.zeros((3, 4, 5, 6, 7, 8))
new_arr = arr.reshape(*arr.shape[:2], -1, *arr.shape[-2:])
new_arr.shape
# (3, 4, 30, 7, 8)
回答by kmario23
An alternative approach is to use numpy.resize()as in:
另一种方法是使用numpy.resize()：
In [37]: shp = (50,100,25)
In [38]: arr = np.random.random_sample(shp)
In [45]: resized_arr = np.resize(arr, (np.prod(shp[:2]), shp[-1]))
In [46]: resized_arr.shape
Out[46]: (5000, 25)
# sanity check with other solutions
In [47]: resized = np.reshape(arr, (-1, shp[-1]))
In [48]: np.allclose(resized_arr, resized)
Out[48]: True
