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    <p>You are mixing different concepts, I'm afraid.</p> <p>Your <code>arrtup</code> array is <strong>not</strong> an array of tuples, it's a structured <code>ndarray</code>, that is, an array of elements that look like tuples but in fact are records (<code>numpy.void</code> objects, to be exact). In your case, you defined these records to consist in 2 integers. Internally, NumPy creates your array as a 2x2 array of blocks, each block taking a given space defined by your <code>dtype</code>: here, a block consists of 2 consecutive blocks of size <code>int</code> (that is, each sub-block takes the space an <code>int</code> takes on your machine).</p> <p>When you retrieve an element with <code>arrtup[0,1]</code>, you get the corresponding block. Because this block is structured as two-subblocks, NumPy returns a <code>numpy.void</code> (the generic object representing structured blocks), which has the same <code>dtype</code> as your array.</p> <p>Because you set the size of those blocks at the creation of the array, you're no longer able to modify it. That means that you cannot transform your 2-int records into 4-int ones as you want.</p> <p>However, you can transform you structured array into an array of <em>objects</em>:</p> <pre><code>new_arr = arrtup.astype(object) </code></pre> <p>Lo and behold, your elements are no longer <code>np.void</code> but tuples, that you can modify as you want:</p> <pre><code>new_arr[0,1] = (3,4) # That's a tuple new_arr[0,1] += (4,4) # Adding another tuple to the element </code></pre> <p>Your <code>new_arr</code> is a different beast from your <code>arrtup</code>: it has the same size, true, but it's no longer a structured array, it's an array of objects, as illustrated by</p> <pre><code>&gt;&gt;&gt; new_arr.dtype dtype("object") </code></pre> <p>In practice, the memory layout is quite different between <code>arrtup</code> and <code>newarr</code>. <code>newarr</code> doesn't have the same constraints as <code>arrtup</code>, as the individual elements can have different sizes, but object arrays are not as efficient as structured arrays.</p>
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    1. POOperator + to add a tuple to another tuple stored inside a multidimensional array of tuples
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    1. POOperator + to add a tuple to another tuple stored inside a multidimensional array of tuples
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    1. COThat is a very nice explanation, thanks! So in practical termns, the ndarray is always filled with `numpy.void` objects, regardless of what the `dtype` says. In that case, the `dtype` is only a useful definition in terms of storing data, since I am able to store a tupe of the kind if defined, but not able to retrieve it. Right? I still dont understand what's the point in not being able to just yield the object out of the array in the shape I defined it with .
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    2. COThat's cool! Thank you, so instead of the initial `empty` statement I will extend it to `arrtup=empty((2,2),dtype=('int,int')).astype(object)` or a shorter less specific version of `arrtup=empty((2,2),dtype=object)`. Thank you!
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    3. CO@vint-i-vuit: Yep, using `object` for your `dtype` will let you do what you want. Compare `np.empty((2,2), dtype=(int,int))` and `np.empty((2,2),dtype=object)`
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