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Mar 05, 2019 · To apply median blurring, you can use the medianBlur () method of OpenCV. Consider the following example where we have a salt and pepper noise in the image: import cv2 img = cv2.imread ( "pynoise.png" ) blur_image = cv2.medianBlur (img, 5) This will apply 50% noise in the image along with median blur.

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from itertools import islice def window(seq, n=2): "Returns a sliding window (of width n) over data from the iterable" " s -> (s0,s1,...s[n-1]), (s1,s2,...,sn), ... " it = iter(seq) result = tuple(islice(it, n)) if len(result) == n: yield result for elem in it: result = result[1:] + (elem,) yield result

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How do I implement sliding window algorithm with a window size of 10 and visualize the data iteratively to see spikes/possible outliers in the dataframe, using python? Please help a beginner.

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df.groupby('rank')['salary'].median().reset_index().rename( columns={'rank':'Rank','salary' : 'MedianSalary'}) Aggregate Data by Group using Pandas Groupby. Most of the time we want to have our summary statistics in the same table. We can calculate the mean and median salary, by groups, using the agg method. In this next Pandas groupby example ...