How to join Thailand district data to administrative boundaries
Join by the district PCODE in the same reference release, rather than by a translated district name. Retain unmatched rows and the boundary version so a spelling difference or changed geography cannot silently disappear.
Procedure
Choose province or district granularity before importing the table.
Read identifiers as text, trim accidental whitespace and check uniqueness on both sides.
Join by PCODE, report unmatched identifiers and inspect a sample boundary before publishing.
Interpretation checks
Input or condition
How to interpret it
Name-only join
Bangkok names can have multiple transliterations; a match is ambiguous.
Duplicate PCODE
Check whether your table is a time series before allowing repeated geography.
Unmatched row
Keep it in an exception table; do not assign it to a nearby district.
Illustrative working example
Code illustrates a review procedure, not an observed result. Replace illustrative inputs with verified records; missing values deliberately remain unknown.
input = {"district_code": "TH1001", "value": 12}
# Keep district codes as strings; verify the code exists in
# the chosen boundary release before joining by identifier.
assert isinstance(input["district_code"], str)
Useful output and decision limits
A reproducible table of district identifiers, values and join exceptions. It supports regional reporting, not cadastral parcel ownership.
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