Seonglae ChoObject - 병렬적으로 하려고 했는데 생각해보니 stpano 올라간것만 geoserver 올라가야 하니까 stuplaod 하고 pgupload를 st db 에서 하면 될듯 ㅎ
heading 추가했는데 안되서 보니까 지오서버 레이어에서 heading 안드러와서 그럼
기본적으로 그냥 업데이트 되는데 원래 있던 값들은
칼럼 추가된거는 db에서 가져올때 모르니까 레이어 새로 만들어줘야한다 - 추가방법 있을 지 모르겠지만 뭐 불편한 건 아니니까
비슷하게 가져와서 flow 완성
pandas.DataFrame.to_sql - pandas 1.0.1 documentation
Write records stored in a DataFrame to a SQL database. Databases supported by SQLAlchemy [1] are supported. Tables can be newly created, appended to, or overwritten. Parameters Name of SQL table. consqlalchemy.engine.Engine or sqlite3.Connection Using SQLAlchemy makes it possible to use any DB supported by that library.
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_sql.html
to_sql 함수 바로 dataframe to table
How to iterate over rows in a DataFrame in Pandas?
How to iterate over rows in a DataFrame in Pandas? Iteration in pandas is an anti-pattern, and is something you should only do when you have exhausted every other option. You should not use any function with " iter" in its name for more than a few thousand rows or you will have to get used to a lot of waiting.
https://stackoverflow.com/questions/16476924/how-to-iterate-over-rows-in-a-dataframe-in-pandas
체크해야하니 반복문
pandas.DataFrame.copy - pandas 1.0.1 documentation
Make a copy of this object's indices and data. When (default), a new object will be created with a copy of the calling object's data and indices. Modifications to the data or indices of the copy will not be reflected in the original object (see notes below).
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.copy.html
check flow 완성해서 filter 함수만 완성하면 댐
pandas.DataFrame.drop - pandas 1.0.1 documentation
Drop specified labels from rows or columns. Remove rows or columns by specifying label names and corresponding axis, or by specifying directly index or column names. When using a multi-index, labels on different levels can be removed by specifying the level. Parameters labelssingle label or list-like Index or column labels to drop.
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html