class_docstr: Base class for generator of stats proto from generic data.
class_docstr_tok: ["Base","class","for","generator","of","stats","proto","from","generic","data","."]
docstr: Fills in the histogram with quantile information from the provided array.
  
  Args:
    hist: A Histogram proto message to fill in.
    nums: A list of numbers to create a quantiles histogram from.
docstr_tok: ["Fills","in","the","histogram","with","quantile","information","from","the","provided","array",".","Args",":","hist",":","A","Histogram","proto","message","to","fill","in",".","nums",":","A","list","of","numbers","to","create","a","quantiles","histogram","from","."]
code: def _PopulateQuantilesHistogram(self, hist, nums):
      """Fills in the histogram with quantile information from the provided array.
  
      Args:
        hist: A Histogram proto message to fill in.
        nums: A list of numbers to create a quantiles histogram from.
      """
      if not nums:
          return
      num_quantile_buckets = 10
      quantiles_to_get = [
          x * 100 / num_quantile_buckets
          for x in range(num_quantile_buckets + 1)
      ]
      quantiles = np.percentile(nums, quantiles_to_get)
      hist.type = self.histogram_proto.QUANTILES
      quantiles_sample_count = float(len(nums)) / num_quantile_buckets
      for low, high in zip(quantiles, quantiles[1:]):
          hist.buckets.add(
              low_value=low,
              high_value=high,
              sample_count=quantiles_sample_count,
          )
  
file_path: facets/facets_overview/python/base_generic_feature_statistics_generator.py
imports: import numpy
  import pandas
  import sys
  import warnings
label_desc: Use image node_id 7 for calling the BaseGenericFeatureStatisticsGenerator obj's underlying member method code with example usage: obj._PopulateQuantilesHistogram(hist, nums) without return types