One table lists observation codes and measurements. Another lists the same codes with the places they describe. It is tempting to sort both sheets and copy the place names across. That works only as long as the rows continue to line up exactly—and an absent code can shift everything that follows.
A small matching exercise makes the problem visible before you work with a larger file. Use invented data, keep the originals, and decide what a row in each table represents.
Match by identity, not position
Imagine measurements for codes A17, A18 and A19. The reference table contains A19, A17 and A18 in that order. The first measurement still belongs to A17; its reference happens to be on the second row. An explicit match on the code preserves that relationship without treating row position as identity.
Use the actual identifier supplied by the data documentation. A similar-looking name may not be unique, and removing leading zeros from a code can change the value you are trying to match.
Decide which side may repeat
Three measurements taken at one location may legitimately repeat its location code. The reference table, however, might be intended to describe that location just once. Those are different expectations: many observation rows can match one reference row.
If the reference table repeats A17 twice, one measurement can acquire two matches. Investigate the reason before deleting either row. Perhaps the reference has versions or sublocations, and the intended key needs another field.
The pandas merge documentation describes validation options for one-to-one and many-to-one matches. These checks express an expectation about keys; they do not establish that the underlying measurements are correct.
Keep unmatched rows visible
An observation with no reference label deserves attention, not silent disappearance. In a trial result, include a match-status column and count rows before and after the operation. Unexpected growth suggests multiple matches; a smaller result may have excluded observations.
Check a known match, a missing match and a repeated code by hand. Record what the operation did in each case. If a code column is poorly explained, start with a data dictionary around that ambiguous column. A combined table becomes useful when you can describe its relationships, including the ones that did not resolve.