pac | Scientific Internet access , intelligent diversion | Proxy library

Parsing the data using python pandas.read_csv(filename, sep='\t', header = 0, ...) seems to have parsed the data successfully and from this point anything could be done with it. Closing this out.

Try to append "name" attribute with same value to all radiobuttons :

One way to deserialise the following json is in two stages. This is because 'creds' is a string.

There are several ways of doing this, but at the end of the day it's hard to give you the best sollution without seeing the original tables. As such, we cannot see if there are any Id's separating the types of attributes, or any other kind of category.

But, simply put. Assuming that you only have up to XL, and not XXL - then you can simply count the amount of characters so that if it is 2 or less, it's a size, if it's 3 or more, it's a color.

Then, you can join these two categories together. If that is not possible due to the sizes being above XL, simply define and "Where al.´name´ in ('XS', 'S', 'M', 'L', 'XL', 'XXL') - tag that as a size, then do an complete oposite (where not in) and join both these columns on the first row you show, the ID.

Here is an example:

If you are still using Selenium v3.x then you shouldn't use the Service() and in that case the key executable_path is relevant. In that case the lines of code will be:

A simpler solution would be to extract all the actual data objects (the ones with the bid property) and put them in list. The pd.DataFrame() function can take a list of objects (dicts) and make a nice dataframe out of it:

You could take an abstract approach by using an object as reference to the nested grouping properties and build the structure as wanted.

This approach works for any depth of grouping just by adding wanted keys to the keys array.

I updated the code so you at once scrape first ten sub-pages in to one dataframe. PLEASE note, that the code for scraping is from @Otto_Kässi answer, so all the credit should go to him!!!

If you don't mind running few steps, this might work:

• run all of the next steps till no changes are made (assuming that global replace is not available)
• distribute the magnitudes:

Find: (\++)([a-zA-Z0-9]+)[;,]([a-zA-Z0-9]+)

Replace: \1\2;\1\3

• move to a new line each keyword, by copying the date

Find: ^(\d\d\.\d\d.\d\d\d\d)(\s+)(\+*)([a-zA-Z0-9]+)[;,](\+*)([a-zA-Z0-9]+)(.*)$

Replace: \1 \3\4;\n\1 \5\6\7

• reformat the lines to have the pluses as a digit after the keyword (1 plus)

Find: ^(\d\d\.\d\d.\d\d\d\d)(\s+)(\+{1})([a-zA-Z0-9]+)[;,]

Replace: \1,\4,1

• reformat the 2 pluses

Find: ^(\d\d\.\d\d.\d\d\d\d)(\s+)(\+{2})([a-zA-Z0-9]+)[;,]

Replace: \1,\4,2

• reformat the 3 pluses

Find: ^(\d\d\.\d\d.\d\d\d\d)(\s+)(\+{3})([a-zA-Z0-9]+)[;,]

Replace: \1,\4,3

• reformat the 4 pluses

Find: ^(\d\d\.\d\d.\d\d\d\d)(\s+)(\+{4})([a-zA-Z0-9]+)[;,]

Replace: \1,\4,4

Empty lines you remove with: (\r?\n)(\r?\n)+ => \1

Lines with only dates you remove with: ^(\d\d\.\d\d.\d\d\d\d)(\s*)(\r?\n) => nothing