numpy.histogram_bin_edges(a, bins=10, range=None, weights=None)
[source]
Function to calculate only the edges of the bins used by the histogram
function.
Parameters: 


Returns: 

See also
The methods to estimate the optimal number of bins are well founded in literature, and are inspired by the choices R provides for histogram visualisation. Note that having the number of bins proportional to is asymptotically optimal, which is why it appears in most estimators. These are simply plugin methods that give good starting points for number of bins. In the equations below, is the binwidth and is the number of bins. All estimators that compute bin counts are recast to bin width using the ptp
of the data. The final bin count is obtained from np.round(np.ceil(range / h))
.
The binwidth is proportional to the interquartile range (IQR) and inversely proportional to cube root of a.size. Can be too conservative for small datasets, but is quite good for large datasets. The IQR is very robust to outliers.
The binwidth is proportional to the standard deviation of the data and inversely proportional to cube root of x.size
. Can be too conservative for small datasets, but is quite good for large datasets. The standard deviation is not very robust to outliers. Values are very similar to the FreedmanDiaconis estimator in the absence of outliers.
The number of bins is only proportional to cube root of a.size
. It tends to overestimate the number of bins and it does not take into account data variability.
The number of bins is the base 2 log of a.size
. This estimator assumes normality of data and is too conservative for larger, nonnormal datasets. This is the default method in R’s hist
method.
An improved version of Sturges’ formula that produces better estimates for nonnormal datasets. This estimator attempts to account for the skew of the data.
The simplest and fastest estimator. Only takes into account the data size.
>>> arr = np.array([0, 0, 0, 1, 2, 3, 3, 4, 5]) >>> np.histogram_bin_edges(arr, bins='auto', range=(0, 1)) array([0. , 0.25, 0.5 , 0.75, 1. ]) >>> np.histogram_bin_edges(arr, bins=2) array([0. , 2.5, 5. ])
For consistency with histogram, an array of precomputed bins is passed through unmodified:
>>> np.histogram_bin_edges(arr, [1, 2]) array([1, 2])
This function allows one set of bins to be computed, and reused across multiple histograms:
>>> shared_bins = np.histogram_bin_edges(arr, bins='auto') >>> shared_bins array([0., 1., 2., 3., 4., 5.])
>>> group_id = np.array([0, 1, 1, 0, 1, 1, 0, 1, 1]) >>> hist_0, _ = np.histogram(arr[group_id == 0], bins=shared_bins) >>> hist_1, _ = np.histogram(arr[group_id == 1], bins=shared_bins)
>>> hist_0; hist_1 array([1, 1, 0, 1, 0]) array([2, 0, 1, 1, 2])
Which gives more easily comparable results than using separate bins for each histogram:
>>> hist_0, bins_0 = np.histogram(arr[group_id == 0], bins='auto') >>> hist_1, bins_1 = np.histogram(arr[group_id == 1], bins='auto') >>> hist_0; hist_1 array([1, 1, 1]) array([2, 1, 1, 2]) >>> bins_0; bins_1 array([0., 1., 2., 3.]) array([0. , 1.25, 2.5 , 3.75, 5. ])
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https://docs.scipy.org/doc/numpy1.17.0/reference/generated/numpy.histogram_bin_edges.html