Given a byte buffer, dtype, shape and strides, how to create Numpy ndarray

As mentioned in the comment by @hpaulj, you can accomplish this using the stride_tricks module. You need both np.frombuffer and np.lib.stride_tricks.as_strided:

Gather data from NumPy array

In [1]: import numpy as np
In [2]: x = np.random.random((3, 4)).astype(dtype='f4')
In [3]: buffer = x.data
In [4]: dtype = x.dtype
In [5]: shape = x.shape
In [6]: strides = x.strides

Recreate NumPy array

In [7]: xx = np.frombuffer(buffer, dtype)
In [8]: xx = np.lib.stride_tricks.as_strided(xx, shape, strides)

Verify results

In [9]: x
Out[9]: 
array([[ 0.75343359,  0.20676662,  0.83675659,  0.99904215],
       [ 0.37182721,  0.83846378,  0.6888299 ,  0.57195812],
       [ 0.39905572,  0.7258808 ,  0.88316005,  0.2187883 ]], dtype=float32)

In [10]: xx
Out[10]: 
array([[ 0.75343359,  0.20676662,  0.83675659,  0.99904215],
       [ 0.37182721,  0.83846378,  0.6888299 ,  0.57195812],
       [ 0.39905572,  0.7258808 ,  0.88316005,  0.2187883 ]], dtype=float32)

In [11]: x.strides
Out[11]: (16, 4)
In [12]: xx.strides
Out[12]: (16, 4)

I'd stick with frombuffer because it's intended directly for this purpose, and makes it clear what you're doing. Here's an example:

In [58]: s0 = 'aaaa'   # a single int32

In [59]: s1 = 'aaabaaacaaadaaae'  # 4 int32s, each increasing by 1

In [60]: a0 = np.frombuffer(s0, dtype='>i4', count=1)   # dtype sets the stride

In [61]: print a0
[1633771873]

In [62]: a1 = np.frombuffer(s, dtype='>i4', count=4)

In [63]: print a1
[1633771874 1633771875 1633771876 1633771877]

In [64]: a2 = a1.reshape((2,2))   # do a reshape, which also sets the strides

In [65]: print a2
[[1633771874 1633771875]
 [1633771876 1633771877]]

In [66]: a2 - a0     # do some calculation with the reshape
Out[66]: 
array([[1, 2],
       [3, 4]], dtype=int32)

Is there something you need that this doesn't do?

You could use either method - neither of them will generate a copy:

s = b'aaabaaacaaadaaae'
a1 = np.frombuffer(s, np.int32, 4).reshape(2, 2)
a2 = np.ndarray((2, 2), np.int32, buffer=s)

print(a1.flags.owndata, a1.base.tostring())
# (False, b'aaabaaacaaadaaae')
print(a2.flags.owndata, a2.base)
# (False, b'aaabaaacaaadaaae')

Note that neither array can be modified in place, since they are backed by read-only memory:

a1[:] = 0  # ValueError: assignment destination is read-only