text: #
  # Licensed to the Apache Software Foundation (ASF) under one or more
  # contributor license agreements.  See the NOTICE file distributed with
  # this work for additional information regarding copyright ownership.
  # The ASF licenses this file to You under the Apache License, Version 2.0
  # (the "License"); you may not use this file except in compliance with
  # the License.  You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from __future__ import absolute_import
  
  import math
  import sys
  import unittest
  
  import numpy as np
  import pandas as pd
  
  import apache_beam as beam
  from apache_beam.dataframe import expressions
  from apache_beam.dataframe import frame_base
  from apache_beam.dataframe import frames  # pylint: disable=unused-import
  
  
  class DeferredFrameTest(unittest.TestCase):
    def _run_test(self, func, *args, distributed=False):
      deferred_args = [
          frame_base.DeferredFrame.wrap(
              expressions.ConstantExpression(arg, arg[0:0])) for arg in args
      ]
      expected = func(*args)
      session_type = (
          expressions.PartitioningSession if distributed else expressions.Session)
      actual = session_type({}).evaluate(func(*deferred_args)._expr)
      if hasattr(expected, 'equals'):
        if distributed:
          cmp = lambda df: expected.sort_index().equals(df.sort_index())
        else:
          cmp = expected.equals
      elif isinstance(expected, float):
        cmp = lambda x: (math.isnan(x) and math.isnan(expected)
                         ) or x == expected == 0 or abs(expected - x) / (
                             abs(expected) + abs(x)) < 1e-8
      else:
        cmp = expected.__eq__
      self.assertTrue(
          cmp(actual), 'Expected:\n\n%r\n\nActual:\n\n%r' % (expected, actual))
  
    def test_series_arithmetic(self):
      a = pd.Series([1, 2, 3])
      b = pd.Series([100, 200, 300])
      self._run_test(lambda a, b: a - 2 * b, a, b)
  
    def test_get_column(self):
      df = pd.DataFrame({
          'Animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'],
          'Speed': [380., 370., 24., 26.]
      })
      self._run_test(lambda df: df['Animal'], df)
      self._run_test(lambda df: df.Speed, df)
  
    def test_set_column(self):
      def new_column(df):
        df['NewCol'] = df['Speed']
        return df
  
      df = pd.DataFrame({
          'Animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'],
          'Speed': [380., 370., 24., 26.]
      })
      self._run_test(new_column, df)
  
    def test_groupby(self):
      df = pd.DataFrame({'group': ['a', 'a', 'a', 'b'], 'value': [1, 2, 3, 5]})
      self._run_test(lambda df: df.groupby('group').agg(sum), df)
      self._run_test(lambda df: df.groupby('group').sum(), df)
      self._run_test(lambda df: df.groupby('group').median(), df)
  
    @unittest.skipIf(sys.version_info <= (3, ), 'differing signature')
    def test_merge(self):
      # This is from the pandas doctests, but fails due to re-indexing being
      # order-sensitive.
      df1 = pd.DataFrame({
          'lkey': ['foo', 'bar', 'baz', 'foo'], 'value': [1, 2, 3, 5]
      })
      df2 = pd.DataFrame({
          'rkey': ['foo', 'bar', 'baz', 'foo'], 'value': [5, 6, 7, 8]
      })
      with beam.dataframe.allow_non_parallel_operations():
        self._run_test(
            lambda df1,
            df2: df1.merge(df2, left_on='lkey', right_on='rkey').rename(
                index=lambda x: '*').sort_values(['value_x', 'value_y']),
            df1,
            df2)
        self._run_test(
            lambda df1,
            df2: df1.merge(
                df2,
                left_on='lkey',
                right_on='rkey',
                suffixes=('_left', '_right')).rename(index=lambda x: '*').
            sort_values(['value_left', 'value_right']),
            df1,
            df2)
  
    def test_series_getitem(self):
      s = pd.Series([x**2 for x in range(10)])
      self._run_test(lambda s: s[...], s, distributed=True)
      self._run_test(lambda s: s[:], s, distributed=True)
      self._run_test(lambda s: s[s < 10], s, distributed=True)
      self._run_test(lambda s: s[lambda s: s < 10], s, distributed=True)
  
      s.index = s.index.map(float)
      self._run_test(lambda s: s[1.5:6], s, distributed=True)
  
    def test_dataframe_getitem(self):
      df = pd.DataFrame({'A': [x**2 for x in range(6)], 'B': list('abcdef')})
      self._run_test(lambda df: df['A'], df, distributed=True)
      self._run_test(lambda df: df[['A', 'B']], df, distributed=True)
  
      self._run_test(lambda df: df[:], df, distributed=True)
      self._run_test(lambda df: df[df.A < 10], df, distributed=True)
  
      df.index = df.index.map(float)
      self._run_test(lambda df: df[1.5:4], df, distributed=True)
  
    def test_loc(self):
      dates = pd.date_range('1/1/2000', periods=8)
      df = pd.DataFrame(
          np.arange(32).reshape((8, 4)),
          index=dates,
          columns=['A', 'B', 'C', 'D'])
      self._run_test(lambda df: df.loc[:], df)
      self._run_test(lambda df: df.loc[:, 'A'], df)
      self._run_test(lambda df: df.loc[:dates[3]], df)
      self._run_test(lambda df: df.loc[df.A > 10], df)
      self._run_test(lambda df: df.loc[lambda df: df.A > 10], df)
  
    def test_series_agg(self):
      s = pd.Series(list(range(16)))
      self._run_test(lambda s: s.agg('sum'), s)
      self._run_test(lambda s: s.agg(['sum']), s)
      with beam.dataframe.allow_non_parallel_operations():
        self._run_test(lambda s: s.agg(['sum', 'mean']), s)
        self._run_test(lambda s: s.agg(['mean']), s)
        self._run_test(lambda s: s.agg('mean'), s)
  
    @unittest.skipIf(sys.version_info < (3, 6), 'Nondeterministic dict ordering.')
    def test_dataframe_agg(self):
      df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [2, 3, 5, 7]})
      self._run_test(lambda df: df.agg('sum'), df)
      with beam.dataframe.allow_non_parallel_operations():
        self._run_test(lambda df: df.agg(['sum', 'mean']), df)
        self._run_test(lambda df: df.agg({'A': 'sum', 'B': 'sum'}), df)
        self._run_test(lambda df: df.agg({'A': 'sum', 'B': 'mean'}), df)
        self._run_test(lambda df: df.agg({'A': ['sum', 'mean']}), df)
        self._run_test(lambda df: df.agg({'A': ['sum', 'mean'], 'B': 'min'}), df)
  
    @unittest.skipIf(sys.version_info < (3, 6), 'Nondeterministic dict ordering.')
    def test_smallest_largest(self):
      df = pd.DataFrame({'A': [1, 1, 2, 2], 'B': [2, 3, 5, 7]})
      self._run_test(lambda df: df.nlargest(1, 'A', keep='all'), df)
      self._run_test(lambda df: df.nsmallest(3, 'A', keep='all'), df)
      self._run_test(lambda df: df.nlargest(3, ['A', 'B'], keep='all'), df)
  
    def test_series_cov_corr(self):
      for s in [pd.Series([1, 2, 3]),
                pd.Series(range(100)),
                pd.Series([x**3 for x in range(-50, 50)])]:
        self._run_test(lambda s: s.std(), s, distributed=True)
        self._run_test(lambda s: s.corr(s), s, distributed=True)
        self._run_test(lambda s: s.corr(s + 1), s, distributed=True)
        self._run_test(lambda s: s.corr(s * s), s, distributed=True)
        self._run_test(lambda s: s.cov(s * s), s, distributed=True)
  
    def test_dataframe_cov_corr(self):
      df = pd.DataFrame(np.random.randn(20, 3), columns=['a', 'b', 'c'])
      df.loc[df.index[:5], 'a'] = np.nan
      df.loc[df.index[5:10], 'b'] = np.nan
      self._run_test(lambda df: df.corr().round(8), df, distributed=True)
      self._run_test(lambda df: df.cov().round(8), df, distributed=True)
      self._run_test(
          lambda df: df.corr(min_periods=12).round(8), df, distributed=True)
      self._run_test(
          lambda df: df.cov(min_periods=12).round(8), df, distributed=True)
      self._run_test(lambda df: df.corrwith(df.a).round(8), df, distributed=True)
      self._run_test(
          lambda df: df[['a', 'b']].corrwith(df[['b', 'c']]).round(8),
          df,
          distributed=True)
  
  
  class AllowNonParallelTest(unittest.TestCase):
    def _use_non_parallel_operation(self):
      _ = frame_base.DeferredFrame.wrap(
          expressions.PlaceholderExpression(pd.Series([1, 2, 3]))).replace(
              'a', 'b', limit=1)
  
    def test_disallow_non_parallel(self):
      with self.assertRaises(expressions.NonParallelOperation):
        self._use_non_parallel_operation()
  
    def test_allow_non_parallel_in_context(self):
      with beam.dataframe.allow_non_parallel_operations():
        self._use_non_parallel_operation()
  
    def test_allow_non_parallel_nesting(self):
      # disallowed
      with beam.dataframe.allow_non_parallel_operations():
        # allowed
        self._use_non_parallel_operation()
        with beam.dataframe.allow_non_parallel_operations(False):
          # disallowed again
          with self.assertRaises(expressions.NonParallelOperation):
            self._use_non_parallel_operation()
        # allowed
        self._use_non_parallel_operation()
      # disallowed
      with self.assertRaises(expressions.NonParallelOperation):
        self._use_non_parallel_operation()
  
  
  if __name__ == '__main__':
    unittest.main()
  