code: def test_simple_preprocessor_dirty_float():
      dirty = pd.DataFrame(make_dirty_float())
      fp = EasyPreprocessor()
      fp.fit(dirty)
      res = fp.transform(dirty)
      assert res.shape == (100, 3)
      rowsum = res.sum(axis=0)
      # count of "garbage"
      assert rowsum[1] == 1
      # count of "missing"
      assert rowsum[2] == 9
  
      # make sure we can transform a clean column
      fp.transform(pd.DataFrame(["0", "1", "2"], columns=["a_column"]))
  
imports: import os
  import pytest
  import random
  import string
  import warnings
  import numpy
  import pandas
  from sklearn.datasets import load_iris, load_digits
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import make_pipeline
  from sklearn.linear_model import LogisticRegression
  from dabl.preprocessing import USELESS_TYPES, detect_types, EasyPreprocessor, DirtyFloatCleaner, clean, _FLOAT_REGEX, _float_matching, detect_type_series
  from dabl.utils import data_df_from_bunch
  from dabl.datasets import load_titanic
  from dabl import plot
label_desc: Use image node_id 18 for calling a global function with example usage: test_simple_preprocessor_dirty_float() without return types