18337 | 18.337 - Parallel Computing and Scientific Machine Learning | GPU library

You are almost there:

You are mixing 2 different ways of doing forecasts. you either use fable or you use forecast. auto.arima is from the forecast package. Though it does work with fable, it is better to keep everything to the same package eco system. Fable is the successor of forecast. Your library loading problably conflicted somewhere.

For arima forecasts check out chapter 9.7 from Forecasting: Principles and Practice 3rd edition.

I adjusted your code to work with fable. I have included 2 ways of doing this. My preference is the second one, because then you can see the difference in AICc values and see that they are very close to each other.

You were close but I think this is what you were after:

Use numpy.argwhere. This acts like where, but returns the indicies found instead of the elements

You can use a loop to load the object after sorting it. I prefer using order() so I can extract the sort position for other uses, but a simple sort() is good. The legend for plotly is based on when you add_trace/add_bars to the plotly, the earlier one gets to the top position.

Since you only used 2 y's, I assume the user can only select up to 2 "Names" at a time, and you are trying to make sure the Countries are sorted:

I think this would do the trick:

You can extract year-month from the date, aggregate and plot -

Try the complete.cases() function. From the description: "Return a logical vector indicating which cases are complete, i.e., have no missing values.

Edit: I hadn't noticed that the missing values were represented by ".". In order for this to work you'll need to replace the periods with proper NA values, as demonstrated in IRTMF's post.

Unsure for the efficiency, because this will rely an the apply method, but you could concat the dataframe with itself, adding a 'A' column to the first and a 'B' one to the second. Then you sort the resulting dataframe by its index, and use apply to change even rows to the first value of the tuple and odd ones to the second: