class_docstr: Logs statistics about training run into Pandas dataframes.
  Records three separate dataframes: train, eval, epoch.
  
  Parameters
  ----------
  batch_size: int
      batch_size of data
  frequent: int
      How many training mini-batches between calculations.
      Defaults to calculating every 50 batches.
      (Eval data is stored once per epoch over the entire
      eval data set.)
class_docstr_tok: ["Logs","statistics","about","training","run","into","Pandas","dataframes",".","Records","three","separate","dataframes",":","train",",","eval",",","epoch",".","Parameters","--","--","--","--","--","batch_size",":","int","batch_size","of","data","frequent",":","int","How","many","training","mini-batches","between","calculations",".","Defaults","to","calculating","every","50","batches",".","(","Eval","data","is","stored","once","per","epoch","over","the","entire","eval","data","set",".",")"]
docstr: Update parameters for selected dataframe after a completed batch
  Parameters
  ----------
  dataframe : pandas.DataFrame
      Selected dataframe needs to be modified.
code: def _process_batch(self, param, dataframe):
      """Update parameters for selected dataframe after a completed batch
      Parameters
      ----------
      dataframe : pandas.DataFrame
          Selected dataframe needs to be modified.
      """
      now = time.time()
      if param.eval_metric is not None:
          metrics = dict(param.eval_metric.get_name_value())
          param.eval_metric.reset()
      else:
          metrics = {}
      # #11504
      try:
          speed = self.frequent / (now - self.last_time)
      except ZeroDivisionError:
          speed = float("inf")
      metrics["batches_per_sec"] = speed * self.batch_size
      metrics["records_per_sec"] = speed
      metrics["elapsed"] = self.elapsed()
      metrics["minibatch_count"] = param.nbatch
      metrics["epoch"] = param.epoch
      self.append_metrics(metrics, dataframe)
      self.last_time = now
  
imports: import time
label_desc: Use image node_id 10 for calling the PandasLogger obj's underlying member method code with example usage: obj._process_batch(param, dataframe) without return types