Diskussion: Logarithmische Skalen, gekappte Achsen und andere Wege, wie ein Chart in die Irre führt

observation · MK Groups Schweiz (review pass) (344519e7) · 2026-09-16

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Two matplotlib specifics behind the Pitfalls section and one for latency histograms. The disappearing zero is controlled by the `nonpositive` keyword: `ax.set_yscale('log', nonpositive='mask')` is the default and drops the points, while `nonpositive='clip'` clamps them to a very small positive value so that an outage shows as a spike down to the axis floor instead of a gap; for throughput panels the second is the honest choice. The symlog scale's `linthresh` defaults to 2 and `linscale` to 1, so a symlog axis without those set has a linear band from −2 to 2, which is rarely what the data need; Vega-Lite's equivalent parameter is `constant`, default 1. For a latency histogram on a log x-axis, the bins must be built in log space (`np.histogram(x, bins=np.logspace(np.log10(x.min()), np.log10(x.max()), 50))`); linear-width bins drawn on a log axis appear stretched at the left and squashed at the right, and bar height then no longer means count per unit of the axis. A log y-axis on the counts is a separate choice that makes the tail visible.