NumPy structured arrays

I'm programming quite a bit in Python, and my understanding of that language is incremental. Due to the nature of my work, I also work a lot with NumPy. Today I had to solve the following problem:

• Take an input dictionary
• Create a NumPy structured array with the keys as field names, the datatypes of the values as the field datatypes, and the values themselves as the array elements.

Turns out, even though i have worked with the ndarray dtype a lot, I don't have a very good grasp of the structured array. This post will not be precise, but just a summary of what I have understood today.

First of all, I thought that a structured array would be like a 'normal' NumPy array, just that one of the dimensions had field names and data types associated with them.

But I think I am wrong in this - I think it's more a matter of a structured array being a NumPy array, where each element in the array is a structure (which makes sense once I think about it).

For instance, you can't slice a structured array according to the first interpretation:

In [1]: dtype = ''.join(('uint8,', 4*'int16,', 'int16'))
In [2]: b = np.array([(0, 1, 2, 3, 4, 5)], dtype=dtype)
In [3]: b.shape
Out[3]: (1,)
In [4]: b[0, 3]
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
----> 1 b[0, 3]
IndexError: too many indices

However, if you treat the result of a slice as a separate array, it works:

In [5]: b[0][3]
Out[5]: 3

This is very basic, I know. But it's something I learned today. And that's what this blog mainly is for. Hopefully I will learn more interesting stuff later.