code: def test_covariance_update_many_sampled():
      # NOTE: this test only works with ddof=1 because pandas ignores it if there are missing values
      ddof = 1
      cov = covariance.EmpiricalCovariance(ddof=ddof)
      p = 5
      X_all = pd.DataFrame(columns=range(p))
  
      for _ in range(p):
          n = np.random.randint(5, 31)
          X = pd.DataFrame(np.random.random((n, p))).sample(
              p - 1, axis="columns"
          )
  
          cov.update_many(X)
  
          X_all = pd.concat((X_all, X)).astype(float)
          pd_cov = X_all.cov(ddof=ddof)
  
          for i, j in cov._cov:
              assert math.isclose(cov[i, j].get(), pd_cov.loc[i, j])
  
file_path: river/river/covariance/test_emp.py
imports: from __future__ import annotations
  import math
  import random
  import numpy
  import pandas
  import pytest
  from river import covariance, stream
label_desc: Use image node_id 6 for calling a global function with example usage: test_covariance_update_many_sampled() without return types