content: import pandas as pd
  
  
   # Set up column renaming for Polars
  def assert_equal_dfs(pandas_df, polars_df):
      """Helper function to compare Pandas and Polars dataframes."""
      renamer = {"unique_id": "uid", "ds": "time", "y": "target"}
      inverse_renamer = {v: k for k, v in renamer.items()}
      mapping = {k: v for k, v in inverse_renamer.items() if k in polars_df}
      pd.testing.assert_frame_equal(
          pandas_df,
          polars_df.rename(mapping).to_pandas(),
      )
  
  def get_expected_size(df, h, test_size, step_size):
      expected_size = 0
      uids = df["unique_id"].unique()
      for uid in uids:
          input_len = len(df[df["unique_id"] == uid])
          expected_size += ((input_len - test_size - h) / step_size + 1) * h
      return expected_size
  