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Count most frequent group with Nan values basically I would like to count number of the most frequent item grouped by 2 variables. I use this code: dfgrouped = data[COLUMNS.copy()].groupby(['Var1','Var2']).agg(lambda x: stats.mode(x)[1]) This code works, but does not work on columns that have Nan values, since NaN values are float and others are str. So this error is shown: '<' not supported between instances of 'float' and 'str' I would like to omit NaN values and count mode for the rest. So str(x) is not a solution. And scipy.stats.mode(x, nan_policy='omit') does not work neither with an error: TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'' Could you please give me an advice how to deal with that. Thanks 3 Answers 3 ...

Why does this order of the Gaussian filter in scipy give the x and y derivative? I'm using a Gaussian filter with Scipy and I saw this code online which I'm curious about. imx = zeros(im.shape) filters.gaussian_filter(im, (sigma,sigma), (0,1), imx) imy = zeros(im.shape) filters.gaussian_filter(im, (sigma,sigma), (1,0), imy) For the first Gaussian filter call, the order is (0,1) and according to this link, that should give the the first order derivative of a Gaussian in y-direction. However, on running the code, I can see that the Gaussian is along the X direction. The same thing applies to imy. Why does the code work that way? For reference, running: filters.gaussian_filter(im, (sigma, sigma), (0, 1), output= imx) on this array: [[0 3 2] [1 4 1] [3 4 2]] Returns: [[0.00071801 0.00148952 0.00077151] [0.0006947 0.00144284 0.00074815] [0.00067141 0.00139622 0.00072482]] Which is a Gaussian in the x direction, even though the order (0, 1) suggests that it should be in the y dire...