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 : zipline.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 (zipline.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".
docstr: Load data from our stored baseline.
docstr_tok: ["Load","data","from","our","stored","baseline","."]
code: def load_adjusted_array(self, domain, columns, dates, sids, mask):
      """
      Load data from our stored baseline.
      """
      if len(columns) != 1:
          raise ValueError(
              "Can't load multiple columns with DataFrameLoader"
          )
  
      column = columns[0]
      self._validate_input_column(column)
  
      date_indexer = self.dates.get_indexer(dates)
      assets_indexer = self.assets.get_indexer(sids)
  
      # Boolean arrays with True on matched entries
      good_dates = date_indexer != -1
      good_assets = assets_indexer != -1
  
      data = self.baseline[ix_(date_indexer, assets_indexer)]
      mask = (good_assets & as_column(good_dates)) & mask
  
      # Mask out requested columns/rows that didn't match.
      data[~mask] = column.missing_value
  
      return {
          column: AdjustedArray(
              # Pull out requested columns/rows from our baseline data.
              data=data,
              adjustments=self.format_adjustments(dates, sids),
              missing_value=column.missing_value,
          ),
      }
  
file_path: zipline/zipline/pipeline/loaders/frame.py
imports: from functools import partial
  from interface import implements
  from numpy import ix_, zeros
  from pandas import DataFrame, DatetimeIndex, Index, Int64Index
  from zipline.lib.adjusted_array import AdjustedArray
  from zipline.lib.adjustment import make_adjustment_from_labels
  from zipline.utils.numpy_utils import as_column
  from .base import PipelineLoader
label_desc: Use image node_id 3 for calling the DataFrameLoader obj's underlying member method code with example usage: obj.load_adjusted_array(domain, columns, dates, sids, mask) and returns: dict