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Plt.hist

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)

Plt.hist has 5 facts recorded in Dontopedia across 2 references.

5 facts·5 predicates·2 sources

Mostly:requires(1), rdfs:label(1), rdf:type(1)

Maturity scale raw canonical shape-checked rule-derived certified

Requiresrequires

Rdfs:labelrdfs:label

  • Matplotlib Histogram Function[1]all time · 49edf2e9 8b64 412a 9e57 De713505c895

Rdf:typerdf:type

Specified Edge ColorspecifiedEdgeColor

  • black[2]sourceall time · 9e7b4505 0e17 45e0 B233 Db0dd53d364a

Specified BinsspecifiedBins

  • 50[2]sourceall time · 9e7b4505 0e17 45e0 B233 Db0dd53d364a

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

5 undated
—
labelbeam/49edf2e9-8b64-412a-9e57-de713505c895
Matplotlib Histogram Function
—
typebeam/49edf2e9-8b64-412a-9e57-de713505c895
ex:PlottingFunction
—
requiresbeam/49edf2e9-8b64-412a-9e57-de713505c895
ex:complexities
—
specifiedBinsbeam/9e7b4505-0e17-45e0-b233-db0dd53d364a
50
—
specifiedEdgeColorbeam/9e7b4505-0e17-45e0-b233-db0dd53d364a
black

References (2)

2 references
  1. [1]beam-chunk3 facts
    customctx:claims/beam/49edf2e9-8b64-412a-9e57-de713505c895
    • full textbeam-chunk
      text/plain1 KBdoc:beam/49edf2e9-8b64-412a-9e57-de713505c895
      Show excerpt
      First, analyze the distribution of your query complexities to identify natural breakpoints or regions where the data density changes significantly. ```python import numpy as np import matplotlib.pyplot as plt # Define the complexities com…
  2. [2]beam-chunk2 facts
    customctx:claims/beam/9e7b4505-0e17-45e0-b233-db0dd53d364a
    • full textbeam-chunk
      text/plain1 KBdoc:beam/9e7b4505-0e17-45e0-b233-db0dd53d364a
      Show excerpt
      import matplotlib.pyplot as plt # Simulation parameters num_queries = 1000 latency_mean = 300 # ms latency_stddev = 50 # ms query_distribution = np.random.uniform(0, 1, num_queries) # Simulate latency latencies = np.random.normal(latenc…

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