I have a dataframe The logic is if col1 is not zero, return col1. If col 1 is zero, return col2 (non-zero). If col 2 is zero, return col3. We don’t need to do anything for col4 My code looks like below but it only returns col1 I tried .any() and .all(), it doesnt work either. Also, is there anyway
Frog Jump Memoization(Python)
Below is the problem description, followed by its recursive solution using Python. This solution is inefficient. I know using memoization, we can improve this solution. I have also gone through similar questions on StackOverflow but I couldn’t figure out the solution. I am fairly new to the memoization technique. If anyone could help me with the solution using memoization, that
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I’m working on a webscraping code, and the process is quite long. Thus, I would like to start and stop it in a fancy way, with a button using tkinter. But I couldn’t find any solution on the web. The .after() function doesn’t seems to be a good option as I don’t want to restart my loop each time. My
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I created two functions that make two specific plots and returns me the respective figures: Now, outside of my functions, I want to create a figure with two subplots and add my function figures to it. Something like this: However, just allocating the created figures to this new figure does not work. The function calls the figure, but it is
Detect GPS spikes in location data
I have a Dataset from the GPS log of my Google Account, from which I’d like to remove outliers from the CSV that clearly are not meant to be there. For example, the GPS shows you are at 1,1 > 1,2 > 9,6 > 1,2 > 1,1, so a major variation in location, that a couple seconds later is back
How do you best mark node as visited in Depth First Search?
In some algorithms, like DFS, we need to mark nodes as visited. If I have a Node class, in Python we can write something like node.visited = True. However when I do that, I change the nodes which might be undesired. So what would be the best and cleanest approach here? Things I thought of: delete the attribute at the
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