code: def _expected_data(self, asset_finder):
      sids = {
          symbol: asset_finder.lookup_symbol(
              symbol,
              self.asset_start,
          ).sid
          for symbol in self.symbols
      }
  
      def per_symbol(symbol):
          df = pd.read_csv(
              test_resource_path(
                  "csvdir_samples",
                  "csvdir",
                  "daily",
                  symbol + ".csv.gz",
              ),
              parse_dates=["date"],
              index_col="date",
              usecols=[
                  "open",
                  "high",
                  "low",
                  "close",
                  "volume",
                  "date",
                  "dividend",
                  "split",
              ],
              na_values=["NA"],
          )
          df["sid"] = sids[symbol]
          return df
  
      all_ = (
          pd.concat(map(per_symbol, self.symbols))
          .set_index(
              "sid",
              append=True,
          )
          .unstack()
      )
  
      # fancy list comprehension with statements
      @list
      @apply
      def pricing():
          for column in self.columns:
              vs = all_[column].values
              if column == "volume":
                  vs = np.nan_to_num(vs)
              yield vs
  
      adjustments = [
          [
              5572,
              5576,
              5595,
              5634,
              5639,
              5659,
              5698,
              5699,
              5701,
              5702,
              5722,
              5760,
              5764,
              5774,
              5821,
              5822,
              5829,
              5845,
              5884,
              5885,
              5888,
              5908,
              5947,
              5948,
              5951,
              5972,
              6011,
              6020,
              6026,
              6073,
              6080,
              6096,
              6135,
              6136,
              6139,
              6157,
              6160,
              6198,
              6199,
              6207,
              6223,
              6263,
              6271,
              6277,
          ],
          [
              5572,
              5576,
              5595,
              5634,
              5639,
              5659,
              5698,
              5699,
              5701,
              5702,
              5722,
              5760,
              5764,
              5774,
              5821,
              5822,
              5829,
              5845,
              5884,
              5885,
              5888,
              5908,
              5947,
              5948,
              5951,
              5972,
              6011,
              6020,
              6026,
              6073,
              6080,
              6096,
              6135,
              6136,
              6139,
              6157,
              6160,
              6198,
              6199,
              6207,
              6223,
              6263,
              6271,
              6277,
          ],
          [
              5572,
              5576,
              5595,
              5634,
              5639,
              5659,
              5698,
              5699,
              5701,
              5702,
              5722,
              5760,
              5764,
              5774,
              5821,
              5822,
              5829,
              5845,
              5884,
              5885,
              5888,
              5908,
              5947,
              5948,
              5951,
              5972,
              6011,
              6020,
              6026,
              6073,
              6080,
              6096,
              6135,
              6136,
              6139,
              6157,
              6160,
              6198,
              6199,
              6207,
              6223,
              6263,
              6271,
              6277,
          ],
          [
              5572,
              5576,
              5595,
              5634,
              5639,
              5659,
              5698,
              5699,
              5701,
              5702,
              5722,
              5760,
              5764,
              5774,
              5821,
              5822,
              5829,
              5845,
              5884,
              5885,
              5888,
              5908,
              5947,
              5948,
              5951,
              5972,
              6011,
              6020,
              6026,
              6073,
              6080,
              6096,
              6135,
              6136,
              6139,
              6157,
              6160,
              6198,
              6199,
              6207,
              6223,
              6263,
              6271,
              6277,
          ],
          [5701, 6157],
      ]
  
      return pricing, adjustments
  
file_path: zipline/tests/data/bundles/test_csvdir.py
imports: from __future__ import division
  import numpy
  import pandas
  from trading_calendars import get_calendar
  from zipline.data.bundles import ingest, load, bundles
  from zipline.testing import test_resource_path
  from zipline.testing.fixtures import ZiplineTestCase
  from zipline.testing.predicates import assert_equal
  from zipline.utils.functional import apply
label_desc: Use image node_id 1 for calling the CSVDIRBundleTestCase obj's underlying member method code with example usage: obj._expected_data(asset_finder) and returns: pricing,  adjustments, df