James Stroud wrote:
> Gerard Flanagan wrote:
> >> py> # the following is probably the trickiest, should it return a Table
> >> py> # should it be illegal?
> >> py> # should t['Last'] be the way to take the "slice" and get the col?
> >> py> t[None, 'Last'] # 1d slice returns list (2nd dim. explicit)
> >> ['Barker', 'Burnet', 'Danson', 'Cooper']
> >
> > I can imagine manipulating columns at the Table creation stage -
> > insert, append, delete column - but after that I think you would be
> > dealing with rows more often. Personally, if I needed columns I would
> > be happier with a list comprehension:
> > [ (row['Last'], row['Age']) for row in t ]
> > etc.
>
> To make a table from list comprehension in this way seems like it would
> require some redundancy because a list comprehension only gets you a
> list (of rows or lists). It seems if you wanted to work with your 2d
> selection of data, then you would want to get a table back:
>
> data = [ (row['Last'], row['Age']) for row in t ]
> t2 = Table(('Last','Age'), data)
>
> This seems, to me, to separates selection in the 2 dimensions and makes
> it "backwards":
>
> data = [ (row['Last'], row['Age']) for row in t[1:3]]
> t2 = Table(('Last','Age'), data)
>
> So a function would be needed to group the selection in a way that
> reflects the organization of the table:
>
> t2 = t.subtable((1,3), ('Last','Age'))
>
> But then, since the latter seems a natural consequence of using list
> comprehension for selection, how might one distinguish a range of
> columns if not by a verbose function name?
>
> t2 = t.subtable_with_column_range((1,3), ('Last','Age'))
>
> The concept of slicing seems to take care of this. Maybe
>
> t2 = t.subtable(slice(1,3), slice('Last','Age'))
>
> But this begins to seem awkward and verbose to boot. Any suggestions on
> adapting your idea of list comprehension to generate subtables?
>
What about symmetric 'load' and 'iterrows' methods for the Table class:
t2 = Table()
t2.load( t1.iterrows("LastName", "Age") )
def load(self, iterable):
'''expecting tuples'''
for lname, age in iterable:
self.append( lname, age )
def iterrows(self, *args):
'''returns a generator which itself returns tuples
filtered by the column names in *args (via
operator.itemgetter maybe?)'''
There might be some value in comparing Microsoft's DataSet API. IIRC,
you typically have:
*UI* - DataView - DataSet - DataAdapter - *DATABASE*
A DataSet is a collection of DataTables and has a lot of DB/XML
functionality and so on. A DataView on the other hand is more
lightweight and has mainly filtering and sorting functions and is
designed to be bound to a GUI widget such as a DataGrid.
You might have something along the lines of:
ds = DataSet()
# fill ds from datasource
dt_customers = ds.tables["customers"]
view = DataView( dt_customers )
view.rowfilter = "lastname LIKE 'A*' and Age < 60"
I'm not saying imitate this, but maybe some value in studying the
OO-bondage approach.
All the best
Gerard
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