old_contents: import numpy as np
  import pandas as pd
  try:
      import pandas.core.computation.expressions as expr
  except ImportError:
      import pandas.computation.expressions as expr
  
  
  class Eval(object):
  
      params = [['numexpr', 'python'], [1, 'all']]
      param_names = ['engine', 'threads']
  
      def setup(self, engine, threads):
          self.df = pd.DataFrame(np.random.randn(20000, 100))
          self.df2 = pd.DataFrame(np.random.randn(20000, 100))
          self.df3 = pd.DataFrame(np.random.randn(20000, 100))
          self.df4 = pd.DataFrame(np.random.randn(20000, 100))
  
          if threads == 1:
              expr.set_numexpr_threads(1)
  
      def time_add(self, engine, threads):
          pd.eval('self.df + self.df2 + self.df3 + self.df4', engine=engine)
  
      def time_and(self, engine, threads):
          pd.eval('(self.df > 0) & (self.df2 > 0) & '
                  '(self.df3 > 0) & (self.df4 > 0)', engine=engine)
  
      def time_chained_cmp(self, engine, threads):
          pd.eval('self.df < self.df2 < self.df3 < self.df4', engine=engine)
  
      def time_mult(self, engine, threads):
          pd.eval('self.df * self.df2 * self.df3 * self.df4', engine=engine)
  
      def teardown(self, engine, threads):
          expr.set_numexpr_threads()
  
  
  class Query(object):
  
      def setup(self):
          N = 10**6
          halfway = (N // 2) - 1
          index = pd.date_range('20010101', periods=N, freq='T')
          s = pd.Series(index)
          self.ts = s.iloc[halfway]
          self.df = pd.DataFrame({'a': np.random.randn(N), 'dates': s},
                                 index=index)
          data = np.random.randn(N)
          self.min_val = data.min()
          self.max_val = data.max()
  
      def time_query_datetime_index(self):
          self.df.query('index < @self.ts')
  
      def time_query_datetime_column(self):
          self.df.query('dates < @self.ts')
  
      def time_query_with_boolean_selection(self):
          self.df.query('(a >= @self.min_val) & (a <= @self.max_val)')
  
  
  from .pandas_vb_common import setup  # noqa: F401
  
new_contents: import numpy as np
  import pandas as pd
  try:
      import pandas.core.computation.expressions as expr
  except ImportError:
      import pandas.computation.expressions as expr
  
  
  class Eval(object):
  
      params = [['numexpr', 'python'], [1, 'all']]
      param_names = ['engine', 'threads']
  
      def setup(self, engine, threads):
          self.df = pd.DataFrame(np.random.randn(20000, 100))
          self.df2 = pd.DataFrame(np.random.randn(20000, 100))
          self.df3 = pd.DataFrame(np.random.randn(20000, 100))
          self.df4 = pd.DataFrame(np.random.randn(20000, 100))
  
          if threads == 1:
              expr.set_numexpr_threads(1)
  
      def time_add(self, engine, threads):
          pd.eval('self.df + self.df2 + self.df3 + self.df4', engine=engine)
  
      def time_and(self, engine, threads):
          pd.eval('(self.df > 0) & (self.df2 > 0) & '
                  '(self.df3 > 0) & (self.df4 > 0)', engine=engine)
  
      def time_chained_cmp(self, engine, threads):
          pd.eval('self.df < self.df2 < self.df3 < self.df4', engine=engine)
  
      def time_mult(self, engine, threads):
          pd.eval('self.df * self.df2 * self.df3 * self.df4', engine=engine)
  
      def teardown(self, engine, threads):
          expr.set_numexpr_threads()
  
  
  class Query(object):
  
      def setup(self):
          N = 10**6
          halfway = (N // 2) - 1
          index = pd.date_range('20010101', periods=N, freq='T')
          s = pd.Series(index)
          self.ts = s.iloc[halfway]
          self.df = pd.DataFrame({'a': np.random.randn(N), 'dates': index},
                                 index=index)
          data = np.random.randn(N)
          self.min_val = data.min()
          self.max_val = data.max()
  
      def time_query_datetime_index(self):
          self.df.query('index < @self.ts')
  
      def time_query_datetime_column(self):
          self.df.query('dates < @self.ts')
  
      def time_query_with_boolean_selection(self):
          self.df.query('(a >= @self.min_val) & (a <= @self.max_val)')
  
  
  from .pandas_vb_common import setup  # noqa: F401
  
subject: fix typo in asv eval.Query suite (#24865)