As of 2020, there is a better method than the one in the accepted answer. The matplotlib.axes.Axes class provides a bxp method, which can be used to draw the boxes and whiskers based on the percentile values. Raw data is only needed for the outliers, and that is optional. Example: import matplotlib.pyplot as plt fig, … Read more
As of 2020, there is a better method than the one in the accepted answer. The matplotlib.axes.Axes class provides a bxp method, which can be used to draw the boxes and whiskers based on the percentile values. Raw data is only needed for the outliers, and that is optional. Example: import matplotlib.pyplot as plt fig, … Read more
Completely vectorized numpy solution Here is the code I use. It’s not an optimal one (which I’m unable to write with numpy), but still much faster and more reliable than accepted solution def weighted_quantile(values, quantiles, sample_weight=None, values_sorted=False, old_style=False): “”” Very close to numpy.percentile, but supports weights. NOTE: quantiles should be in [0, 1]! :param values: … Read more
You might be interested in the SciPy Stats package. It has the percentile function you’re after and many other statistical goodies. percentile() is available in numpy too. import numpy as np a = np.array([1,2,3,4,5]) p = np.percentile(a, 50) # return 50th percentile, e.g median. print p 3.0 This ticket leads me to believe they won’t … Read more
I think your example input/output does not correspond to typical ways of calculating percentile. If you calculate the percentile as “proportion of data points strictly less than this value”, then the top value should be 0.8 (since 4 of 5 values are less than the largest one). If you calculate it as “percent of data … Read more
So i haven worked that much with HashMaps, but looking at the JavaDoc it should be something like Hashmap<Integer><String> h; int sum = 0; for(int i: h.keySet()) { sum += i; } double percentile = sum/h.size()