class_docstr: A PipelineLoader that reads its input from DataFrames.
  
  Mostly useful for testing, but can also be used for real work if your data
  fits in memory.
  
  Parameters
  ----------
  column : catalyst.pipeline.data.BoundColumn
      The column whose data is loadable by this loader.
  baseline : pandas.DataFrame
      A DataFrame with index of type DatetimeIndex and columns of type
      Int64Index.  Dates should be labelled with the first date on which a
      value would be **available** to an algorithm.  This means that OHLCV
      data should generally be shifted back by a trading day before being
      supplied to this class.
  
  adjustments : pandas.DataFrame, default=None
      A DataFrame with the following columns:
          sid : int
          value : any
          kind : int (catalyst.pipeline.loaders.frame.ADJUSTMENT_TYPES)
          start_date : datetime64 (can be NaT)
          end_date : datetime64 (must be set)
          apply_date : datetime64 (must be set)
  
      The default of None is interpreted as "no adjustments to the baseline".
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code: def __init__(self, column, baseline, adjustments=None):
      self.column = column
      self.baseline = baseline.values.astype(self.column.dtype)
      self.dates = baseline.index
      self.assets = baseline.columns
  
      self._columns = self.assets
  
      if adjustments is None:
          adjustments = DataFrame(
              index=DatetimeIndex([]),
              columns=ADJUSTMENT_COLUMNS,
          )
      else:
          # Ensure that columns are in the correct order.
          adjustments = adjustments.reindex_axis(
              ADJUSTMENT_COLUMNS, axis=1
          )
          adjustments.sort_values(["apply_date", "sid"], inplace=True)
  
      self.adjustments = adjustments
      self.adjustment_apply_dates = DatetimeIndex(
          adjustments.apply_date
      )
      self.adjustment_end_dates = DatetimeIndex(adjustments.end_date)
      self.adjustment_sids = Int64Index(adjustments.sid)
  
file_path: catalyst/catalyst/pipeline/loaders/frame.py
imports: from functools import partial
  from numpy import ix_, zeros
  from pandas import DataFrame, DatetimeIndex, Index, Int64Index
  from catalyst.lib.adjusted_array import AdjustedArray
  from catalyst.lib.adjustment import make_adjustment_from_labels
  from catalyst.utils.numpy_utils import as_column
  from .base import PipelineLoader
label_desc: Use image node_id 1 to create a new DataFrameLoader object from inherited base classes: PipelineLoader with example: obj = DataFrameLoader(column, baseline, adjustments)