Raccoon | Google Play desktop client | Android library

The Google sheet at the URL below contains two worksheets, Sheet1, Sheet2.

Google sheet: https://docs.google.com/spreadsheets/d/1pgdF1hoid8m1Zj3KburwjZRGkydLh1Sum4DshUMaIeo/edit#gid=0

When I use the Query URL #1 below, it successfully returns a JSON file with the correct result for the query: SELECT B where A contains "nut" (the result being the string "crunch")

Query URL #1: https://docs.google.com/spreadsheets/d/1pgdF1hoid8m1Zj3KburwjZRGkydLh1Sum4DshUMaIeo/gviz/tq?tq=SELECT%20B%20where%20A%20contains%20%22nut%22

Please note that the worksheet "sheet2" contains a different table as you can see at: https://docs.google.com/spreadsheets/d/1pgdF1hoid8m1Zj3KburwjZRGkydLh1Sum4DshUMaIeo/edit#gid=1652705509

If I try to query that worksheet with the Query URL #2 shown below, it does not work (it should return a json file with the result "raccoon"). Instead of returning that json file, it only displays that worksheet. What am I doing wrong?

Thank you.

Query url #2: https://docs.google.com/spreadsheets/d/1pgdF1hoid8m1Zj3KburwjZRGkydLh1Sum4DshUMaIeo/edit#gid=1652705509/gviz/tq?tq=SELECT%20B%20where%20A%20contains%20%22bird%22

I am using this API - https://rapidapi.com/rapidapi/api/movie-database-imdb-alternative I am using the JavaScript implementation and I can't see the values I am supposed to. This is not my first work with APIs, but I don't understand this behavior.

My code:

I'm using the Marvel API and I would like to display comic information in a modal after clicking on a comic card but the problem is that:

-the modal displays all the 20 comics description instead of one.

-or modal showed the same description for all 20 comics when clicked on a card.

I guess it has something to do with the comic ID but I don't know how to get the ID nor how to connect it with the modal.  When I try to get it, either I get all Id's or only the first comic's id.

The result of the request looks like this and I don't know how could I connect a card to it's related Id:

I would like to take an existing dataset of incident rate of viral spread at a specific non-geographic location and create a heatmap based on a set of co-ordinates I will feed it for the base image.

In order to do so I would take an existing image of a site, such as Raccoon City below:

On that, I want to superimpose a heatmap similar to this on top of it as numbers increase in certain areas (like Downtown, Raccoon Park, City College of Raccoon):

I've looked at various libraries including OpenCV, and Pillow, but haven't found a well suited solution. I keep seeing references to heatmap.py, but it seems to be totally deprecated; and I can't get it installed with pip.

I can link to a dummy dataset if needed.

Thanks for looking! This might just save us from the imminent zombie apocalypse ( ¬º-°)¬.

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I'm very new to javascript and working on making a random generator that runs in a browser, and I think I have things mostly figured out, except I can't figure out how to ensure there are no repeats between random strings.

I was thinking maybe removing the first result from the array so the second can't pick it (preferable, because in the final code there will be multiple variables pulling from the same array in multiple different configurations, and several arrays being pulled from in this way, but I don't know if that's possible?), or maybe having the second one rerun until it gets something unique.

however, I, uh, don't know how to do either of these things, and no amount of googling has given me anything both relevant and understandable enough (as a beginner) for me to successfully apply ^^; any help?

this is a simplified version of my code to just include relevant stuff, let me know if I need to provide more

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I have an order_portfolio_id which represents the person buying the toys, and I have columns position_X where X is the number of position of the item ordered, with total of 8 positions. Person ordering the toys will never buy the same toy twice, so the items never repeat in one portfolio/row. Please note my original dataframe contains NaN, so I included them here as well.

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