(Pandas) – How to re-write df[“column_A”]` to only return values of a column_A for where the values of another column are equal to ‘3’?

I am pretty new to python, pandas etc. Context: My dataframe ‘listCoords’ which looks as such: Date/Time Lat Lon Threat 2019-01-20 06:00:00, -10.792921094981814, 20.191716715339687, 2 2019-01-20 06:05:00, -11.798684405083584, 21.162881454312586, 3 2019-01-20 06:05:00, -10.798684405083584, 19.162881454312586, 1 2019-01-20 06:05:00, -11.798684405083584, 20.145381454312586, 1 Currently I use all the “lat” values per: lat = listCoords[“Lat”]. Now I want to…

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Can settimeout collide with cleartimeout from eventListener are they executed on the same thread

I have a setTimeout that adds an eventListener, but if an action occur i want to remove the eventListener. let to; function() fun{ to = setTimeout(() => el.addEventListener(‘mouseover’, foo), 500); } fun(); windows.addEventListener(‘mouseup’, function(removeListener){ clearTimeout(to); el.removeEventListener(‘mouseover’, foo) }) I remove the event listener because the code from the timeout could have already started and still…

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How to check all sublists for a set of required elements

A list will contain 9 sublists of 9 numbers. Each sublist must contain the numbers 1-9 in any order (i.e. there must be no repetition/missing numbers.) How to check this condition is met? valid_list would return true, invalid_list would return false. valid_list = [[1,3,5,7,9,8,6,4,2],[1,2,3,4,5,6,7,8,9],[5,4,3,2,1,9,8,7,6], [1,3,5,7,9,8,6,4,2],[1,2,3,4,5,6,7,8,9],[5,4,3,2,1,9,8,7,6], [1,3,5,7,9,8,6,4,2],[1,2,3,4,5,6,7,8,9],[5,4,3,2,1,9,8,7,6]] invalid_list = [[1,1,1,1,1,1,1,1,1],[2,1,2,1,2,1,2,1,2],[9,8,7,7,7,7,6,5,6], [1,1,1,1,1,1,1,1,1],[2,1,2,1,2,1,2,1,2],[9,8,7,7,7,7,6,5,6], [1,1,1,1,1,1,1,1,1],[2,1,2,1,2,1,2,1,2],[9,8,7,7,7,7,6,5,6]] I am sure that…

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How do I aggregate rows with similar strings values as new rows in a pandas DataFrame?

Below is a DataFrame I created using Pandas… ╔════════════════════════╦══════════╗ ║ Column A ║ Column B ║ ╠════════════════════════╬══════════╣ ║ / ║ 5.34 ║ ║ new-shirts ║ 6.78 ║ ║ new-pants ║ 10.11 ║ ║ used-hats ║ 1.56 ║ ║ used-shirts ║ 3.78 ║ ║ brand-new-watches/gold ║ 4.21 ║ ║ customer-service ║ 0.29 ║ ║ holiday-blowout-sale ║…

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