Re: Replace NULL values
От | Sim Zacks |
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Тема | Re: Replace NULL values |
Дата | |
Msg-id | edot55$2vqo$1@news.hub.org обсуждение исходный текст |
Ответ на | Re: Replace NULL values (Stefan Schwarzer <stefan.schwarzer@grid.unep.ch>) |
Ответы |
Re: Replace NULL values
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Список | pgsql-general |
Aside from your database structure being problematic, what are you trying to accomplish? In other words, what do you want to replace the nulls with and in what circumstance? I imagine your table looks like this ID,country,1950,1951,1952,1953,.... 1 usa 50 null 70 10 2 canada 10 45 null 4 Please mention what you would like to do with this? Stefan Schwarzer wrote: >> On Thu, Sep 07, 2006 at 07:45:19AM +0200, Stefan Schwarzer wrote: >>> Hi there, >>> >>> is there a simple way to replace NULL values in multiple columns >>> within the SQL statement? I changed the underlaying country template >>> of your database; so now there are a couple of NULL values when I >>> join the stats-table with the country table. Unfortunately, my >>> queries have always multiple (year) columns, so I can't do a kind of >>> manual replace. >>> >>> I found that the COALESCE command does something like this, but I >>> couldn't figure out how this works. >> >> Yes, COALESCE replaces NULLs, however your examples have neither NULLs >> nor use COALESCE, so I don't understand what your question is. >> >> Please repost with an actual example of your problem. > > As I said, I couldn't figure out how COALESCE would work on multiple > columns (without naming them explicitly). > > So, say I have a table with columns for each year between 1970 and 2005. > For specific countries the values might be NULL, depending if the > statistical table has been updated recently (then they will have a > value), or not (then they will be NULL). A sample query would thus be > something like: > > SELECT * FROM pop_density > > > > > ---------------------------(end of broadcast)--------------------------- > TIP 9: In versions below 8.0, the planner will ignore your desire to > choose an index scan if your joining column's datatypes do not > match >
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