I find the list comprehension much clearer than filter + lambda, but use whichever you find easier. There are two things that may slow down your use of filter. The first is the function call overhead: as soon as you use a Python function (whether created by def or lambda) it is likely that filter will be slower than the list comprehension.
FILTER() will often return a 0 for blank rows, even when a return string is specified. Using filter() I am often getting a 0 return value for empty cells. Assume these 6 rows of data in column A: abc xyz abc xyz abc If I use FILTER(A10:A15, A10:A15 <> "xyz", "") I get back the following (sometimes): abc abc 0 abc This seems to be somewhat ...
You create your filter over A:G by condition of K:K, like you had and you filter the result for the columns in your filtered range being equal to the given columns.
Setting the value of the filter query-string parameter to a string using those delimiters creates a list of name/value pairs which can be parsed easily on the server-side and utilized to enhance database queries as needed.
You can filter by multiple columns (more than two) by using the np.logical_and operator to replace & (or np.logical_or to replace |) Here's an example function that does the job, if you provide target values for multiple fields.
The shape of the filter_list was only a suggestion, so that it is readable. I wouldn't call the filters filter_1, filter_2, etc. but in such a way, that it's clear what the purpose of the filter is. Within each filter it should be clear what column of the data.frame is targeted and what values are selected.
I have a data.frame with character data in one of the columns. I would like to filter multiple options in the data.frame from the same column. Is there an easy way to do this that I'm missing? Exam...
What I would like to do is be able to perform a filter on the object to return a subset of "home" objects. For example, I want to be able to filter based on: price, sqft, num_of_beds, and num_of_baths.
