code: def bench(
      mn=3, mx=7, num=100, engines=("python", "numexpr"), verbose=False
  ):
      r = np.logspace(mn, mx, num=num).round().astype(int)
  
      ev = DataFrame(np.empty((num, len(engines))), columns=engines)
      qu = ev.copy(deep=True)
  
      ev["size"] = qu["size"] = r
  
      for engine in engines:
          for i, n in enumerate(r):
              if verbose & (i % 10 == 0):
                  print(f"engine: {engine!r}, i == {i:d}")
              ev_times = bench_with(n, times=1, repeat=1, engine=engine)
              ev.loc[i, engine] = np.mean(ev_times)
              qu_times = bench_subset(
                  n, times=1, repeat=1, engine=engine
              )
              qu.loc[i, engine] = np.mean(qu_times)
  
      return ev, qu
  
file_path: pandas/doc/scripts/eval_performance.py
imports: from timeit import repeat
  import numpy
  import seaborn
  from pandas import DataFrame
label_desc: Use image node_id 3 for calling a global function with example usage: bench(mn, mx, num, engines, verbose) and returns: ev,  qu