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Choosing the number of bins in a histogram has always been something that gets me thinking a lot. Based on the number of bins chosen, the graph at time looks a lot different and also could lead to different interpretations.

Below is the square-root rule, which I use as the thumb-rule for selecting the number of bins in most occasions.

Posting this question here to hear other opinions.

data_pts = len(np.array(data))
bin_cnt = int(np.sqrt(data_pts))

plt.hist(data, bins=bin_cnt)
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    $\begingroup$ This might help: stats.stackexchange.com/questions/798/… Also there's this Sturges rule whose formula is similar to your code. $\endgroup$ Jan 24 '20 at 0:07
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    $\begingroup$ Thanks @FatemehAsgarinejad, that answer does cover a number of methods. $\endgroup$
    – loki
    Jan 24 '20 at 5:16
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Another method is Bayesian Blocks from Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations by Scargle et al.

Bayesian Blocks is a dynamic histogramming method which optimizes one of several possible fitness functions to determine an optimal binning for data, where the bins are not necessarily uniform width.

Bayesian Blocks for Histograms

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