> left: bulk upload. I've thought of using xls (microsoft
> office's excel files) to make it easy on the client, because
> teaching them xml or yaml isn't the big deal. After thinking
> about it, finding a xls parser module for python might not be
> the problem,
I second the idea of using a plain-text interchange format,
whether CSV as David suggested, or (my preferred) tab-delimited
format. I dislike CSV because it takes extra overhead to
synchronize the flavors of them (how are quotes quoted? are
values quoted? etc). I find TAB format just less messy.
> but how could I take on product images? How can I
> bulk upload those? Ok, I can upload a tar or zip file and have
> it unpacked automatically, but how do I automagically assign
> the images with the product, having in mind that some might
> have 5 pics and others might have 2 or none.
Your idea of uploading a zip/tar archive is a good one. One
would need to go with naming conventions. Images could be named
of the format <part_number>_<id>.[jpg|gif] and then any
corresponding items could be added to your ProductImages table.
This assumes you have a <part_number> column in your upload file.
You might want to report on items in the image-archive that don't
have a corresponding item in the upload file (or aren't already
in the data...might be easier).
To augment Adam's suggestion, a lot of the code can be made
shorter and more readable using Python's tuple-assignment, so
rather than having hard-coded indicies such as "product.slug =
raw_data[6]", you can do something like
(in_id,
manufacturer,
vendor,
dealer_price,
dealer_price_updated,
model,
slug,
description,
package_contents,
...
) = raw_data
You can then use each of these parts for later assignment, or
even directly assign them like
(in_id,
manufacturer,
...
product.model,
product.slug,
product.description,
... ) = raw_data
This does assume a fixed column order, which would require some
tweaks if the columns could be in an arbitrary order. However, a
little dictionary trickery can solve that. I usually do this via
REQUIRED_COLUMNS = [
'manufacturer',
'slug',
...
]
for row_num, row in file(filename):
row = row.rstrip('\n').split(DELIM)
if row_num == 0:
header_to_column = dict([
(n, i) for (i,n)
in enumerate(row)])
header_set = set([h.lower() for h in row])
for header in REQUIRED_COLUMNS:
assert header in header_set, 'Missing column %s' % header
continue
# use header_to_column to map the desired column name
# to its offset/index
-tim
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