code: def clean_individual_ticker(args):
      tic, df, times = args
      tmp_df = pd.DataFrame(index=times)
      tic_df = df[df.tic == tic].set_index("timestamp")
  
      # Step 1: Merging dataframes to avoid loop
      tmp_df = tmp_df.merge(
          tic_df[["open", "high", "low", "close", "volume"]],
          left_index=True,
          right_index=True,
          how="left",
      )
  
      # Step 2: Handling NaN values efficiently
      if pd.isna(tmp_df.iloc[0]["close"]):
          first_valid_index = tmp_df["close"].first_valid_index()
          if first_valid_index is not None:
              first_valid_price = tmp_df.loc[first_valid_index, "close"]
              print(
                  f"The price of the first row for ticker {tic} is NaN. It will be filled with the first valid price."
              )
              tmp_df.iloc[0] = [first_valid_price] * 4 + [
                  0.0
              ]  # Set volume to zero
          else:
              print(
                  f"Missing data for ticker: {tic}. The prices are all NaN. Fill with 0."
              )
              tmp_df.iloc[0] = [0.0] * 5
  
      for i in range(1, tmp_df.shape[0]):
          if pd.isna(tmp_df.iloc[i]["close"]):
              previous_close = tmp_df.iloc[i - 1]["close"]
              tmp_df.iloc[i] = [previous_close] * 4 + [0.0]
  
      # Setting the volume for the market opening timestamp to zero - Not needed
      # tmp_df.loc[tmp_df.index.time == pd.Timestamp("09:30:00").time(), 'volume'] = 0.0
  
      # Step 3: Data type conversion
      tmp_df = tmp_df.astype(float)
  
      tmp_df["tic"] = tic
  
      return tmp_df
  
imports: from __future__ import annotations
  from concurrent.futures import ProcessPoolExecutor
  from concurrent.futures import ThreadPoolExecutor
  import alpaca_trade_api
  import exchange_calendars
  import numpy
  import pandas
  import pytz
  from stockstats import StockDataFrame
label_desc: Use image node_id 4 for calling the AlpacaProcessor obj's underlying member method code with example usage: obj.clean_individual_ticker(args) and returns: tmp_df