The following question is more along the lines of "good practice" rather
than "how do you do it" .
If I have a name:value list of values that I want to read into a dict for
ease of lookup, lets define them as:
name | value
==
ItemOne : 10
ItemOne : 10
ItemOne : 10
ItemOne : 10
ItemTwo
Sounds like you don't need the checking, since the items are all identical,
*but* if the values differ for a particular key, then the behaviour will be
different (earliest with checking, latest without). It you have itemOne:
10, then itemOne: 20, the checked version will have 10 for itemOne
You ca
On 21/02/2014 9:40 am, "Anthony Briggs" wrote:
>
> You can also use the dict() function or dictionary comprehensions to
create your dictionary:
> item = dict( (key, value) for key, value in list )
>
Otherwise written:
item = dict(list)
___
melbourne
Yep, just realised that - if it's stored as a list of key, value pairs.
On 21 February 2014 09:45, William ML Leslie
wrote:
>
> On 21/02/2014 9:40 am, "Anthony Briggs" wrote:
> >
> > You can also use the dict() function or dictionary comprehensions to
> create your dictionary:
> > item = dict
Hi David,
On my machine, on a simple, pathological example, it is significantly
faster (around a factor of 2) to check for duplicates.
However, it depends if you expect to have many duplicates. In the second
example with only one duplicated item, it is faster to not check.
The winner in both cas
David Crisp writes:
> name | value
> ==
> ItemOne : 10
> ItemOne : 10
> ItemOne : 10
> ItemOne : 10
> ItemTwo : 20
> ItemTwo : 20
> ItemTwo : 20
> ItemTwo : 20
> ItemThree : 30
> ItemThree : 30
> ItemThree : 30
> ItemThree : 30
If you're confident there will frequently be duplicated lin
Thanks for the thoughts!
In this case I am reading in data from a SQL database query(pymssql) and
feeding it into an excel spreedsheet (xlwt) Line by line which means I
have to assemble the dict line by line.
And yes, the name i chose in the email to represent what I was doing was
generic.
If the data is in a database, and the rows are truly duplicates (i.e. keys
always map to the same values) you could probably remove the duplicates via
the query before loading the data into Python.
The DISTINCT keyword will do this for you, i.e.
SELECT DISTINCT * FROM x;
Otherwise, a GROUP BY co
David Crisp writes:
> In this case I am reading in data from a SQL database query(pymssql)
If you want to eliminate duplicates from your query, do so with ‘SELECT
DISTINCT’. Then you don't ever get the duplicate rows in the first place :-)
--
\ “Ocean, n. A body of water occupying ab
Ahh :)I also meant to say that whilest the particular fields I am
talking about in this question are the same, data at the end of those
rows is different, and will be treated differently.
I really abstracted my query away from what I am actually trying to do
exactly and more along the li
If the data you need to collapse is being duplicated across multiple rows
due to distinct values in other columns, you could generate that data in a
separate, more focussed query. That way you would avoid having to
de-duplicate that information, but you do have an additional query to
maintain and f
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