Not a complaint about this PR, but I don't think I like this behaviour:

>>> np.dtype([('', 'f4'),('f0', 'i4'),('z', 'i8')])
ValueError: field 'f0' occurs more than once

In #9054, I change this in some cases to be "index within the unnamed values". Is that a good thing?

I think it might be clearer to show these as >>> dt = np.dtype(...), >>> np.zeros(1, dtype=dt)

It seems that arrays.dtypes.rst duplicates a lot of the contents here, and perhaps these should be condensed into a single help page

Missing comma

This is misleading - we should clarify whether "equivalent dtypes" are such that dt1 == dt2, or if simply np.can_cast(dt1, dt2)

Might be tempting to write this in the following form:

>>> np.zeros(3, dtype=dict(names=    ['col1', 'col2'],
...                        formats=  ['i4','f4'],
...                        offsets=  [0, 4],
...                        itemsize= 12)

Which of course, raises the question of whether np.dtype(**dict) should be added as a shorhand for np.dtype(dict)

Aligning the [...] isn't PEP8. It may take a bit of getting used to but eventually the unaligned versions become easier to read.

"array of length two" and omit the comma. The clause after the comma is essential.

"for example, interpreting ..."

comma after "purposes"

Maybe "For simple manipulation of tabular data, other pydata projects, such as pandas, xarray, or DataArray, provide higher-level interfaces that may be more suitable."

"by numpy, but can be manually specified."

Could use a rewrite. I'd probably start a new sentence instead of using "with".

Do we want to number subtitles? Maybe a simple enumerated list would do.

"is an empty string, '', the ..."

"... string and separated ..."

"specifications, all of the same length."

"The optional 'offsets' key is a list of integer byte offsets, one for each field within the structure."

"... to contain all the fields."

Commas rather than parenthesis. I don't know when the current parenthetical scourge originated, but it seems to be everywhere these days :-(

Is there an example of using a scalar array for the rhs? Does scalar array mean 1-D array here?

I guess by "scalar array" I mean "unstructured array", will fix.

Is there an example of that? Which side has the single field, or is that both sides?

I'll try to modify the example, perhaps using different variable names,.

Maybe "The view's" instead of "This view's".

and <- comma

this <- the

Could omit "Importantly".

"such that" <- "so that".

though <- thought

Omit "of".

What does "equivalent" mean?

equal, will fix

dimension <- dimesions.

``True`` ?

What might be the alternative?

Currently in this case we get:

[1]: a = np.zeros(3, dtype='f,f,f')
[2]: b = np.zeros(3, dtype='f,f')
[3]: a == b
FutureWarning: elementwise == comparison failed and returning scalar instead; this will raise an error or perform elementwise comparison in the future

I'll reword the text to more accurately describe what happens.

Maybe "The .... operators always return ..."?

Many or all? Maybe "no other"?

which <- that.

Comma not needed.

which <- that