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The boost-histogram library provides first-class histogram objects in Python. You can compose axes and a storage to fit almost any problem. You can fill, manipulate, slice, and project then, and pass them between other Scikit-HEP libraries like Uproot4, mplhep, and histoprint. Boost-histogram is meant to be the "NumPy" of histogram libraries that others can build on; the "pandas" of histograms is "Hist", a physicist friendly front-end that extends and expands boost-histogram to do plotting and more. An early version of Hist is shown for the first time here.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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| downloads | 4 |

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