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*=====zip that is a zip=====
*=====state that is a state=====
*=====city that is a city=====
The feature Select distinct city is messy. In this way, we first clean feature city before identifying clean datarecords in ptoassigneend_us_extracted and store them in ptoasigneend_us_citylist (775).
Select distinct city records and store them in table citylist (30971). Select distinct city records in ptoassigneend_us_extracted and store them in citylist2 (775). The following patterns can be used to clean feature city. * Feature city is in citylist2ptoassigneend_us_citylist.
Patent Assignment Data Restructure (view source)
Revision as of 15:10, 20 April 2017
, 15:10, 20 April 2017→Identify Clean Data
First, filter out records with city that is a city, zip that is a zip, state that is a state. Note: The consistency between city and state or city and postcode is not checked in this section.
Match the pattern 5-4 or 5 digits.
Select distinct state records with
The output shows that all the records not null or not space are valid state names.
The following methods can be used to identify clean city.
SQL Code:
WHERE city IN (
SELECT citylist
FROM citylist2ptoassigneend_us_citylist); -- SELECT 2574811 !!!(992450 LEFT)
