![]() ![]() Get started by typing or pasting a URL encoded string in the input text area, the tool will automatically decode your input string in real time. But need a conversion (usually taking care of by numpy's constructor and getters) back and forth, from python's internal representation of things, and numpy's.URL Decoder is the #1 online tool for decoding URLs. When you use another dtype, then things are encoded in a way that make sense to numpy, on which numpy can work. They are just raw reference to python objects. But you don't need to do anything to decode them. So long story short, when you encode things (number, strings, whatever) in object, you can't use any numpy function on them. As the num of its name implies, numpy's role is more about numbers, but still, the string data are encoded in the array, not just pointers to opaque python reference to a python object that happens to be a string, but that only python knows about. Not much, because numpy is not very well adapted for string processing. Numpy does understand what it means, and can act on it. So, in other words, when using ![]() # Ä«ased on utf8 standard, we see that the first bytes starts with 3 111 followed by a 0. See utf8 = np.frombuffer(sentence.encode(), dtype=np.uint8) ![]() So what are those 19990, 30028 to the encoded string Unsurprisingly, 19990 is unicode for ä¸ ref here I could also, instead of using a int based example, have compared to can't understand what is in a object array. That means that this is opaque for numpy. ![]() A pointer to those python object, if your wish. So, this is what object means from numpy point of view: id of python object are stored. Likewise with your own array struct.unpack('l', a.tobytes()) We see that each number is encoded with 8 bytes, so 24 bytes all together.Ä«ut watching Ub, that doesn't seem to be just the int64 encoding of 1 2 3. Now, compare that to this U=np.array(, dtype=object) So, nothing surprising here: when we create an array on 3 int32, it contains 12 bytes, that are the 12 bytes representing the values of those int32. You can decode also with struct import struct Which also shows by just watching Tb: we see the 12 bytes, 1,0,0,0 (so 1, on 32 bits, little endian - at least on my machine, probably on yours too), 2,0,0,0 (so 2), 3,0,0,0. But it proves what is stored in numpy's memory: directly the values of the 3 int32. Of course, the last line may seem stupid. See the difference T=np.array(, dtype=np.uint32) And can efficiently be iterated by numpy's internal C code, without having to bother about python internal representation of data) So, an array of 1000 short int, use only 2000 bytes in memory. It just means "other python stuff" (by opposition to other data types, that are converted in numpy, without their internal python wrapping: only the array is wrapped in a python object, not all its content. I think you misunderstand what np.object is. ![]()
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