weighted-average

The DevExpress Grid (XtraGrid) allows grids and their groups to have summary calculations. The available options are Count, Max, Min, Avg, Sum, None and Custom. Has anyone got some sample code that shows how to calculate a weighted average column, based upon the weightings provided as values in another column? I ended up working this out, and will post my solution here in case others find it useful. If a weighted average consists of both a value and a weight per row, then column that contains the value should have the weight GridColumn object assigned to its Tag property. Then, this event

I calculate simple moving average: def sma(data_frame, length=15): # TODO: Be sure about default values of length. smas = data_frame.Close.rolling(window=length, center=False).mean() return smas Using the rolling function is it possible to calculate weighted moving average? As I read in the documentation , I think that I have to pass win_type parameter. But I'm not sure which one I have to choose. Here is a definition for weighted moving average. Thanks in advance, Yeah, that part of pandas really isn't very well documented. I think you might have to use rolling.apply() if you aren't using one

This question is more related to logic than any programming language. If the question is not apt for the forum please do let me know and I will delete this. I have to write a logic to calculate scores for blogs for a Blog Award website. A blog may be nominated for multiple award categories and is peer-reviewed or rated by a Jury on a -1 to 5 scale (-1 to indicate a blog they utterly dislike). Now, a blog can be rated by one or more Jurors. One criterion while calculating final score for a blog is that if a blog is rated positively by more people it should get more weightage (and vice-versa).

I need to calculate 5-star ratings like the one on Amazon website. I have done enough search to find what is the best algorithm, but I am not able to get a proper answer. For example, if these are the ratings 5 star - 252 4 star - 124 3 star - 40 2 star - 29 1 star - 33 totally 478 reviews Amazon has calculated this to be "4.1 out of 5 stars". Can anyone tell me how this figure is arrived at? I am not able to get this just by doing average. That's a weighted average, where you weigh each rating with the number of votes it got: (5*252 + 4*124 + 3*40 + 2*29 + 1*33) / (252+124+40+29+33) = 4.11

I have a dataset with weights for each observation and I want to prepare weighted summaries using groupby but am rusty as to how to best do this. I think it implies a custom aggregation function. My issue is how to properly deal with not item-wise data, but group-wise data. Perhaps it means that it is best to do this in steps rather than in one go. In pseudo-code, I am looking for #first, calculate weighted value for each row: weighted jobs = weight * jobs #then, for each city, sum these weights and divide by the count (sum of weights) for each city: sum(weighted jobs)/sum(weight) I am not