code: def _fix_zeroes(self, df, interval, tz_exchange, prepost):
      # Sometimes Yahoo returns prices=0 or NaN when trades occurred.
      # But most times when prices=0 or NaN returned is because no trades.
      # Impossible to distinguish, so only attempt repair if few or rare.
  
      if df.empty:
          return df
  
      logger = utils.get_yf_logger()
  
      if df.shape[0] == 0:
          if "Repaired?" not in df.columns:
              df["Repaired?"] = False
          return df
  
      intraday = interval[-1] in ("m", "h")
  
      df = df.sort_index()  # important!
      df2 = df.copy()
  
      if df2.index.tz is None:
          df2.index = df2.index.tz_localize(tz_exchange)
      elif df2.index.tz != tz_exchange:
          df2.index = df2.index.tz_convert(tz_exchange)
  
      price_cols = [
          c
          for c in ["Open", "High", "Low", "Close", "Adj Close"]
          if c in df2.columns
      ]
      f_prices_bad = (df2[price_cols] == 0.0) | df2[price_cols].isna()
      df2_reserve = None
      if intraday:
          # Ignore days with >50% intervals containing NaNs
          grp = pd.Series(f_prices_bad.any(axis=1), name="nan").groupby(
              f_prices_bad.index.date
          )
          nan_pct = grp.sum() / grp.count()
          dts = nan_pct.index[nan_pct > 0.5]
          f_zero_or_nan_ignore = np.isin(f_prices_bad.index.date, dts)
          df2_reserve = df2[f_zero_or_nan_ignore]
          df2 = df2[~f_zero_or_nan_ignore]
          f_prices_bad = (df2[price_cols] == 0.0) | df2[
              price_cols
          ].isna()
  
      f_high_low_good = (~df2["High"].isna().to_numpy()) & (
          ~df2["Low"].isna().to_numpy()
      )
      f_change = df2["High"].to_numpy() != df2["Low"].to_numpy()
      f_vol_bad = (
          (df2["Volume"] == 0).to_numpy() & f_high_low_good & f_change
      )
  
      # If stock split occurred, then trading must have happened.
      # I should probably rename the function, because prices aren't zero ...
      if "Stock Splits" in df2.columns:
          f_split = (df2["Stock Splits"] != 0.0).to_numpy()
          if f_split.any():
              f_change_expected_but_missing = f_split & ~f_change
              if f_change_expected_but_missing.any():
                  f_prices_bad[f_change_expected_but_missing] = True
  
      # Check whether worth attempting repair
      f_prices_bad = f_prices_bad.to_numpy()
      f_bad_rows = f_prices_bad.any(axis=1) | f_vol_bad
      if not f_bad_rows.any():
          logger.info(
              "price-repair-missing: No price=0 errors to repair"
          )
          if "Repaired?" not in df.columns:
              df["Repaired?"] = False
          return df
      if f_prices_bad.sum() == len(price_cols) * len(df2):
          # Need some good data to calibrate
          logger.info(
              "price-repair-missing: No good data for calibration so cannot fix price=0 bad data"
          )
          if "Repaired?" not in df.columns:
              df["Repaired?"] = False
          return df
  
      data_cols = price_cols + ["Volume"]
  
      # Mark values to send for repair
      tag = -1.0
      for i in range(len(price_cols)):
          c = price_cols[i]
          df2.loc[f_prices_bad[:, i], c] = tag
      df2.loc[f_vol_bad, "Volume"] = tag
      # If volume=0 or NaN for bad prices, then tag volume for repair
      f_vol_zero_or_nan = (df2["Volume"].to_numpy() == 0) | (
          df2["Volume"].isna().to_numpy()
      )
      df2.loc[
          f_prices_bad.any(axis=1) & f_vol_zero_or_nan, "Volume"
      ] = tag
      # If volume=0 or NaN but price moved in interval, then tag volume for repair
      df2.loc[f_change & f_vol_zero_or_nan, "Volume"] = tag
  
      df2_tagged = df2[data_cols].to_numpy() == tag
      n_before = df2_tagged.sum()
      dts_tagged = df2.index[df2_tagged.any(axis=1)]
      df2 = self._reconstruct_intervals_batch(
          df2, interval, prepost, tag
      )
      df2_tagged = df2[data_cols].to_numpy() == tag
      n_after = df2_tagged.sum()
      dts_not_repaired = df2.index[df2_tagged.any(axis=1)]
      n_fixed = n_before - n_after
      if n_fixed > 0:
          msg = f"{self.ticker}: fixed {n_fixed}/{n_before} value=0 errors in {interval} price data"
          if n_fixed < 4:
              dts_repaired = sorted(
                  list(set(dts_tagged).difference(dts_not_repaired))
              )
              msg += f": {dts_repaired}"
          logger.info("price-repair-missing: " + msg)
  
      if df2_reserve is not None:
          if "Repaired?" not in df2_reserve.columns:
              df2_reserve["Repaired?"] = False
          df2 = pd.concat([df2, df2_reserve]).sort_index()
  
      # Restore original values where repair failed (i.e. remove tag values)
      f = df2[data_cols].to_numpy() == tag
      for j in range(len(data_cols)):
          fj = f[:, j]
          if fj.any():
              c = data_cols[j]
              df2.loc[fj, c] = df.loc[fj, c]
  
      return df2
  
imports: from __future__ import print_function
  import datetime
  from io import StringIO
  import json
  import logging
  import time
  import warnings
  from typing import Optional
  from urllib.parse import quote
  import dateutil
  import numpy
  import pandas
  import requests
  from .None import shared, utils, cache
  from .data import YfData
  from .scrapers.analysis import Analysis
  from .scrapers.fundamentals import Fundamentals
  from .scrapers.holders import Holders
  from .scrapers.quote import Quote, FastInfo
  from .const import _BASE_URL_, _ROOT_URL_
label_desc: Use image node_id 7 for calling the TickerBase obj's underlying member method code with example usage: obj._fix_zeroes(df, interval, tz_exchange, prepost) and returns: df2, df, df, df, df