code: def test_replace_dataframe_different_woodwork_initialized(es):
      df = es["customers"].copy()
      if ps and isinstance(df, ps.DataFrame):
          df["age"] = [1, 2, 3]
      else:
          df["age"] = pd.Series([1, 2, 3])
  
      # Initialize Woodwork on the new DataFrame and change the schema so it won't match the original DataFrame's schema
      df.ww.init(schema=es["customers"].ww.schema)
      df.ww.set_types(
          logical_types={
              "id": "NaturalLanguage",
              "cancel_date": "NaturalLanguage",
          },
      )
      assert df["id"].dtype == "string"
      assert df["cancel_date"].dtype == "string"
  
      assert es["customers"]["id"].dtype == "int64"
      assert es["customers"]["cancel_date"].dtype == "datetime64[ns]"
  
      original_schema = es["customers"].ww.schema
  
      warning = "Woodwork typing information on new dataframe will be replaced with existing typing information from customers"
      with pytest.warns(UserWarning, match=warning):
          es.replace_dataframe("customers", df, already_sorted=True)
  
      actual = to_pandas(es["customers"]["age"]).sort_values()
      assert all(actual == [1, 2, 3])
  
      assert es["customers"].ww._schema == original_schema
      assert es["customers"]["id"].dtype == "int64"
      assert es["customers"]["cancel_date"].dtype == "datetime64[ns]"
  
imports: from datetime import datetime
  import numpy
  import pandas
  import pytest
  from woodwork.exceptions import TypeConversionError
  from woodwork.logical_types import Boolean, Categorical, Datetime, Double, Integer, NaturalLanguage
  from featuretools.entityset.entityset import LTI_COLUMN_NAME, EntitySet
  from featuretools.tests.testing_utils import to_pandas
  from featuretools.utils.gen_utils import Library, import_or_none, is_instance
label_desc: Use image node_id 45 for calling a global function with example usage: test_replace_dataframe_different_woodwork_initialized(es) without return types