Why gaps in fields with an (x) for example S0101. I know what the note says about (x), but it makes no sense in many cases.

It may be helpful to know that the percentages in those columns are age distribution within that sub population. In other words, if you sum down the % male column across ages, you'll get 100%. Same for summing down the % female column. Not sure what the coding reason is behind the scenes for those Xs since they could be replaced by 100%. (Maybe early testing showed that when the table showed 100% in that cell people then assumed males represented 100% of the population, and then looked over a couple of columns and wondered how females could also represent 100% of the population?)

It may be helpful to know that the percentages in those columns are age distribution within that sub population. In other words, if you sum down the % male column across ages, you'll get 100%. Same for summing down the % female column. Not sure what the coding reason is behind the scenes for those Xs since they could be replaced by 100%. (Maybe early testing showed that when the table showed 100% in that cell people then assumed males represented 100% of the population, and then looked over a couple of columns and wondered how females could also represent 100% of the population?)

Just so you know it's just not the 100% that is missing, it's the male % female % etc.  (which is actually what I was trying for).  So they could\should clean this up, but this won't happen.  I assumed it was just "good enough for government work". But wanted to make sure I wasn't missing something basic. There are some other tables that have data, but the field descriptions don't match what I expect (percent of something else or another level etc.). That will wait for another day.

At least I understand the API URL format now and have this little excel sheet where I input field names and it builds the API for me. So tired of doing it manually...

I know it's cells for % male and % female that's missing. What I wrote is that if it were filled in, it would be 100% because what that table shows is percent by age *within the sex category.* If they were to fill the top of the column in with, for example, 49.5% percent male and 50.5% female, that would be mixing and matching denominators within the column. Changing denominators within column would lead readers astray on how to interpret the values farther down the column which are NOT sex percent of total population in a given age group but rather are % distribution by age *within the sex category.*

If you are interested in percent male/percent female, rather than age distribution, use a different table: DP05.