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How to use _run_test method in avocado
Best Python code snippet using avocado_python
frames_test.py
Source:frames_test.py 
...93 def wrapper(df):94 df = df.copy()95 func(df)96 return df97 self._run_test(wrapper, arg, **kwargs)98 def _run_test(99 self, func, *args, distributed=True, nonparallel=False, check_proxy=True):100 """Verify that func(*args) produces the same result in pandas and in Beam.101 Args:102 distributed (bool): Whether or not to use PartitioningSession to103 simulate parallel execution.104 nonparallel (bool): Whether or not this function contains a105 non-parallelizable operation. If True, the expression will be106 generated twice, once outside of an allow_non_parallel_operations107 block (to verify NonParallelOperation is raised), and again inside108 of an allow_non_parallel_operations block to actually generate an109 expression to verify.110 check_proxy (bool): Whether or not to check that the proxy of the111 generated expression matches the actual result, defaults to True.112 This option should NOT be set to False in tests added for new113 operations if at all possible. Instead make sure the new operation114 produces the correct proxy. This flag only exists as an escape hatch115 until existing failures can be addressed (BEAM-12379)."""116 # Compute expected value117 expected = func(*args)118 # Compute actual value119 deferred_args = _get_deferred_args(*args)120 if nonparallel:121 # First run outside a nonparallel block to confirm this raises as expected122 with self.assertRaises(expressions.NonParallelOperation) as raised:123 func(*deferred_args)124 if raised.exception.msg.startswith(125 "Encountered non-parallelizable form of"):126 raise AssertionError(127 "Default NonParallelOperation raised, please specify a reason in "128 "the Singleton() partitioning requirement for this operation."129 ) from raised.exception130 # Re-run in an allow non parallel block to get an expression to verify131 with beam.dataframe.allow_non_parallel_operations():132 expr = func(*deferred_args)._expr133 else:134 expr = func(*deferred_args)._expr135 # Compute the result of the generated expression136 session_type = (137 expressions.PartitioningSession if distributed else expressions.Session)138 actual = session_type({}).evaluate(expr)139 # Verify140 if isinstance(expected, pd.core.generic.NDFrame):141 if distributed:142 if expected.index.is_unique:143 expected = expected.sort_index()144 actual = actual.sort_index()145 else:146 expected = expected.sort_values(list(expected.columns))147 actual = actual.sort_values(list(actual.columns))148 if isinstance(expected, pd.Series):149 pd.testing.assert_series_equal(expected, actual)150 elif isinstance(expected, pd.DataFrame):151 pd.testing.assert_frame_equal(expected, actual)152 else:153 raise ValueError(154 f"Expected value is a {type(expected)},"155 "not a Series or DataFrame.")156 else:157 # Expectation is not a pandas object158 if isinstance(expected, float):159 if np.isnan(expected):160 cmp = np.isnan161 else:162 cmp = lambda x: np.isclose(expected, x)163 else:164 cmp = lambda x: x == expected165 self.assertTrue(166 cmp(actual), 'Expected:\n\n%r\n\nActual:\n\n%r' % (expected, actual))167 if check_proxy:168 # Verify that the actual result agrees with the proxy169 proxy = expr.proxy()170 if type(actual) in (np.float32, np.float64):171 self.assertTrue(type(actual) == type(proxy) or np.isnan(proxy))172 else:173 self.assertEqual(type(actual), type(proxy))174 if isinstance(expected, pd.core.generic.NDFrame):175 if isinstance(expected, pd.Series):176 self.assertEqual(actual.dtype, proxy.dtype)177 self.assertEqual(actual.name, proxy.name)178 elif isinstance(expected, pd.DataFrame):179 pd.testing.assert_series_equal(actual.dtypes, proxy.dtypes)180 else:181 raise ValueError(182 f"Expected value is a {type(expected)},"183 "not a Series or DataFrame.")184 self.assertEqual(actual.index.names, proxy.index.names)185 for i in range(actual.index.nlevels):186 self.assertEqual(187 actual.index.get_level_values(i).dtype,188 proxy.index.get_level_values(i).dtype)189class DeferredFrameTest(_AbstractFrameTest):190 """Miscellaneous tessts for DataFrame operations."""191 def test_series_arithmetic(self):192 a = pd.Series([1, 2, 3])193 b = pd.Series([100, 200, 300])194 self._run_test(lambda a, b: a - 2 * b, a, b)195 self._run_test(lambda a, b: a.subtract(2).multiply(b).divide(a), a, b)196 def test_dataframe_arithmetic(self):197 df = pd.DataFrame({'a': [1, 2, 3], 'b': [100, 200, 300]})198 df2 = pd.DataFrame({'a': [3000, 1000, 2000], 'b': [7, 11, 13]})199 self._run_test(lambda df, df2: df - 2 * df2, df, df2)200 self._run_test(201 lambda df, df2: df.subtract(2).multiply(df2).divide(df), df, df2)202 @unittest.skipIf(PD_VERSION < (1, 3), "dropna=False is new in pandas 1.3")203 def test_value_counts_dropna_false(self):204 df = pd.DataFrame({205 'first_name': ['John', 'Anne', 'John', 'Beth'],206 'middle_name': ['Smith', pd.NA, pd.NA, 'Louise']207 })208 # TODO(BEAM-12495): Remove the assertRaises this when the underlying bug in209 # https://github.com/pandas-dev/pandas/issues/36470 is fixed.210 with self.assertRaises(NotImplementedError):211 self._run_test(lambda df: df.value_counts(dropna=False), df)212 def test_get_column(self):213 df = pd.DataFrame({214 'Animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'],215 'Speed': [380., 370., 24., 26.]216 })217 self._run_test(lambda df: df['Animal'], df)218 self._run_test(lambda df: df.Speed, df)219 self._run_test(lambda df: df.get('Animal'), df)220 self._run_test(lambda df: df.get('FOO', df.Animal), df)221 def test_series_xs(self):222 # pandas doctests only verify DataFrame.xs, here we verify Series.xs as well223 d = {224 'num_legs': [4, 4, 2, 2],225 'num_wings': [0, 0, 2, 2],226 'class': ['mammal', 'mammal', 'mammal', 'bird'],227 'animal': ['cat', 'dog', 'bat', 'penguin'],228 'locomotion': ['walks', 'walks', 'flies', 'walks']229 }230 df = pd.DataFrame(data=d)231 df = df.set_index(['class', 'animal', 'locomotion'])232 self._run_test(lambda df: df.num_legs.xs('mammal'), df)233 self._run_test(lambda df: df.num_legs.xs(('mammal', 'dog')), df)234 self._run_test(lambda df: df.num_legs.xs('cat', level=1), df)235 self._run_test(236 lambda df: df.num_legs.xs(('bird', 'walks'), level=[0, 'locomotion']),237 df)238 def test_set_column(self):239 def new_column(df):240 df['NewCol'] = df['Speed']241 df = pd.DataFrame({242 'Animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'],243 'Speed': [380., 370., 24., 26.]244 })245 self._run_inplace_test(new_column, df)246 def test_set_column_from_index(self):247 def new_column(df):248 df['NewCol'] = df.index249 df = pd.DataFrame({250 'Animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'],251 'Speed': [380., 370., 24., 26.]252 })253 self._run_inplace_test(new_column, df)254 def test_tz_localize_ambiguous_series(self):255 # This replicates a tz_localize doctest:256 # s.tz_localize('CET', ambiguous=np.array([True, True, False]))257 # But using a DeferredSeries instead of a np array258 s = pd.Series(259 range(3),260 index=pd.DatetimeIndex([261 '2018-10-28 01:20:00', '2018-10-28 02:36:00', '2018-10-28 03:46:00'262 ]))263 ambiguous = pd.Series([True, True, False], index=s.index)264 self._run_test(265 lambda s,266 ambiguous: s.tz_localize('CET', ambiguous=ambiguous),267 s,268 ambiguous)269 def test_tz_convert(self):270 # This replicates a tz_localize doctest:271 # s.tz_localize('CET', ambiguous=np.array([True, True, False]))272 # But using a DeferredSeries instead of a np array273 s = pd.Series(274 range(3),275 index=pd.DatetimeIndex([276 '2018-10-27 01:20:00', '2018-10-27 02:36:00', '2018-10-27 03:46:00'277 ],278 tz='Europe/Berlin'))279 self._run_test(lambda s: s.tz_convert('America/Los_Angeles'), s)280 def test_sort_index_columns(self):281 df = pd.DataFrame({282 'c': range(10),283 'a': range(10),284 'b': range(10),285 np.nan: range(10),286 })287 self._run_test(lambda df: df.sort_index(axis=1), df)288 self._run_test(lambda df: df.sort_index(axis=1, ascending=False), df)289 self._run_test(lambda df: df.sort_index(axis=1, na_position='first'), df)290 def test_where_callable_args(self):291 df = pd.DataFrame(292 np.arange(10, dtype=np.int64).reshape(-1, 2), columns=['A', 'B'])293 self._run_test(294 lambda df: df.where(lambda df: df % 2 == 0, lambda df: df * 10), df)295 def test_where_concrete_args(self):296 df = pd.DataFrame(297 np.arange(10, dtype=np.int64).reshape(-1, 2), columns=['A', 'B'])298 self._run_test(299 lambda df: df.where(300 df % 2 == 0, pd.Series({301 'A': 123, 'B': 456302 }), axis=1),303 df)304 def test_combine_dataframe(self):305 df = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})306 df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})307 take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2308 self._run_test(309 lambda df,310 df2: df.combine(df2, take_smaller),311 df,312 df2,313 nonparallel=True)314 def test_combine_dataframe_fill(self):315 df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})316 df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})317 take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2318 self._run_test(319 lambda df1,320 df2: df1.combine(df2, take_smaller, fill_value=-5),321 df1,322 df2,323 nonparallel=True)324 def test_combine_Series(self):325 s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0})326 s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0})327 self._run_test(328 lambda s1,329 s2: s1.combine(s2, max),330 s1,331 s2,332 nonparallel=True,333 check_proxy=False)334 def test_combine_first_dataframe(self):335 df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})336 df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})337 self._run_test(lambda df1, df2: df1.combine_first(df2), df1, df2)338 def test_combine_first_series(self):339 s1 = pd.Series([1, np.nan])340 s2 = pd.Series([3, 4])341 self._run_test(lambda s1, s2: s1.combine_first(s2), s1, s2)342 def test_add_prefix(self):343 df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})344 s = pd.Series([1, 2, 3, 4])345 self._run_test(lambda df: df.add_prefix('col_'), df)346 self._run_test(lambda s: s.add_prefix('col_'), s)347 def test_add_suffix(self):348 df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})349 s = pd.Series([1, 2, 3, 4])350 self._run_test(lambda df: df.add_suffix('_col'), df)351 self._run_test(lambda s: s.add_prefix('_col'), s)352 def test_set_index(self):353 df = pd.DataFrame({354 # [19, 18, ..]355 'index1': reversed(range(20)), # [15, 16, .., 0, 1, .., 13, 14]356 'index2': np.roll(range(20), 5), # ['', 'a', 'bb', ...]357 'values': [chr(ord('a') + i) * i for i in range(20)],358 })359 self._run_test(lambda df: df.set_index(['index1', 'index2']), df)360 self._run_test(lambda df: df.set_index(['index1', 'index2'], drop=True), df)361 self._run_test(lambda df: df.set_index('values'), df)362 self._run_error_test(lambda df: df.set_index('bad'), df)363 self._run_error_test(364 lambda df: df.set_index(['index2', 'bad', 'really_bad']), df)365 def test_set_axis(self):366 df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, index=['X', 'Y', 'Z'])367 self._run_test(lambda df: df.set_axis(['I', 'II'], axis='columns'), df)368 self._run_test(lambda df: df.set_axis([0, 1], axis=1), df)369 self._run_inplace_test(370 lambda df: df.set_axis(['i', 'ii'], axis='columns'), df)371 with self.assertRaises(NotImplementedError):372 self._run_test(lambda df: df.set_axis(['a', 'b', 'c'], axis='index'), df)373 self._run_test(lambda df: df.set_axis([0, 1, 2], axis=0), df)374 def test_series_set_axis(self):375 s = pd.Series(list(range(3)), index=['X', 'Y', 'Z'])376 with self.assertRaises(NotImplementedError):377 self._run_test(lambda s: s.set_axis(['a', 'b', 'c']), s)378 self._run_test(lambda s: s.set_axis([1, 2, 3]), s)379 def test_series_drop_ignore_errors(self):380 midx = pd.MultiIndex(381 levels=[['lama', 'cow', 'falcon'], ['speed', 'weight', 'length']],382 codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]])383 s = pd.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3], index=midx)384 # drop() requires singleton partitioning unless errors are ignored385 # Add some additional tests here to make sure the implementation works in386 # non-singleton partitioning.387 self._run_test(lambda s: s.drop('lama', level=0, errors='ignore'), s)388 self._run_test(lambda s: s.drop(('cow', 'speed'), errors='ignore'), s)389 self._run_test(lambda s: s.drop('falcon', level=0, errors='ignore'), s)390 def test_dataframe_drop_ignore_errors(self):391 midx = pd.MultiIndex(392 levels=[['lama', 'cow', 'falcon'], ['speed', 'weight', 'length']],393 codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]])394 df = pd.DataFrame(395 index=midx,396 columns=['big', 'small'],397 data=[[45, 30], [200, 100], [1.5, 1], [30, 20], [250, 150], [1.5, 0.8],398 [320, 250], [1, 0.8], [0.3, 0.2]])399 # drop() requires singleton partitioning unless errors are ignored400 # Add some additional tests here to make sure the implementation works in401 # non-singleton partitioning.402 self._run_test(403 lambda df: df.drop(index='lama', level=0, errors='ignore'), df)404 self._run_test(405 lambda df: df.drop(index=('cow', 'speed'), errors='ignore'), df)406 self._run_test(407 lambda df: df.drop(index='falcon', level=0, errors='ignore'), df)408 self._run_test(409 lambda df: df.drop(index='cow', columns='small', errors='ignore'), df)410 def test_merge(self):411 # This is from the pandas doctests, but fails due to re-indexing being412 # order-sensitive.413 df1 = pd.DataFrame({414 'lkey': ['foo', 'bar', 'baz', 'foo'], 'value': [1, 2, 3, 5]415 })416 df2 = pd.DataFrame({417 'rkey': ['foo', 'bar', 'baz', 'foo'], 'value': [5, 6, 7, 8]418 })419 self._run_test(420 lambda df1,421 df2: df1.merge(df2, left_on='lkey', right_on='rkey').rename(422 index=lambda x: '*'),423 df1,424 df2,425 nonparallel=True,426 check_proxy=False)427 self._run_test(428 lambda df1,429 df2: df1.merge(430 df2, left_on='lkey', right_on='rkey', suffixes=('_left', '_right')).431 rename(index=lambda x: '*'),432 df1,433 df2,434 nonparallel=True,435 check_proxy=False)436 def test_merge_left_join(self):437 # This is from the pandas doctests, but fails due to re-indexing being438 # order-sensitive.439 df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})440 df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})441 self._run_test(442 lambda df1,443 df2: df1.merge(df2, how='left', on='a').rename(index=lambda x: '*'),444 df1,445 df2,446 nonparallel=True,447 check_proxy=False)448 def test_merge_on_index(self):449 # This is from the pandas doctests, but fails due to re-indexing being450 # order-sensitive.451 df1 = pd.DataFrame({452 'lkey': ['foo', 'bar', 'baz', 'foo'], 'value': [1, 2, 3, 5]453 }).set_index('lkey')454 df2 = pd.DataFrame({455 'rkey': ['foo', 'bar', 'baz', 'foo'], 'value': [5, 6, 7, 8]456 }).set_index('rkey')457 self._run_test(458 lambda df1,459 df2: df1.merge(df2, left_index=True, right_index=True),460 df1,461 df2,462 check_proxy=False)463 def test_merge_same_key(self):464 df1 = pd.DataFrame({465 'key': ['foo', 'bar', 'baz', 'foo'], 'value': [1, 2, 3, 5]466 })467 df2 = pd.DataFrame({468 'key': ['foo', 'bar', 'baz', 'foo'], 'value': [5, 6, 7, 8]469 })470 self._run_test(471 lambda df1,472 df2: df1.merge(df2, on='key').rename(index=lambda x: '*'),473 df1,474 df2,475 nonparallel=True,476 check_proxy=False)477 self._run_test(478 lambda df1,479 df2: df1.merge(df2, on='key', suffixes=('_left', '_right')).rename(480 index=lambda x: '*'),481 df1,482 df2,483 nonparallel=True,484 check_proxy=False)485 def test_merge_same_key_doctest(self):486 df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})487 df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})488 self._run_test(489 lambda df1,490 df2: df1.merge(df2, how='left', on='a').rename(index=lambda x: '*'),491 df1,492 df2,493 nonparallel=True,494 check_proxy=False)495 # Test without specifying 'on'496 self._run_test(497 lambda df1,498 df2: df1.merge(df2, how='left').rename(index=lambda x: '*'),499 df1,500 df2,501 nonparallel=True,502 check_proxy=False)503 def test_merge_same_key_suffix_collision(self):504 df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2], 'a_lsuffix': [5, 6]})505 df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4], 'a_rsuffix': [7, 8]})506 self._run_test(507 lambda df1,508 df2: df1.merge(509 df2, how='left', on='a', suffixes=('_lsuffix', '_rsuffix')).rename(510 index=lambda x: '*'),511 df1,512 df2,513 nonparallel=True,514 check_proxy=False)515 # Test without specifying 'on'516 self._run_test(517 lambda df1,518 df2: df1.merge(df2, how='left', suffixes=('_lsuffix', '_rsuffix')).519 rename(index=lambda x: '*'),520 df1,521 df2,522 nonparallel=True,523 check_proxy=False)524 def test_value_counts_with_nans(self):525 # similar to doctests that verify value_counts, but include nan values to526 # make sure we handle them correctly.527 df = pd.DataFrame({528 'num_legs': [2, 4, 4, 6, np.nan, np.nan],529 'num_wings': [2, 0, 0, 0, np.nan, 2]530 },531 index=['falcon', 'dog', 'cat', 'ant', 'car', 'plane'])532 self._run_test(lambda df: df.value_counts(), df)533 self._run_test(lambda df: df.value_counts(normalize=True), df)534 if PD_VERSION >= (1, 3):535 # dropna=False is new in pandas 1.3536 # TODO(BEAM-12495): Remove the assertRaises this when the underlying bug537 # in https://github.com/pandas-dev/pandas/issues/36470 is fixed.538 with self.assertRaises(NotImplementedError):539 self._run_test(lambda df: df.value_counts(dropna=False), df)540 # Test the defaults.541 self._run_test(lambda df: df.num_wings.value_counts(), df)542 self._run_test(lambda df: df.num_wings.value_counts(normalize=True), df)543 self._run_test(lambda df: df.num_wings.value_counts(dropna=False), df)544 # Test the combination interactions.545 for normalize in (True, False):546 for dropna in (True, False):547 self._run_test(548 lambda df,549 dropna=dropna,550 normalize=normalize: df.num_wings.value_counts(551 dropna=dropna, normalize=normalize),552 df)553 def test_value_counts_does_not_support_sort(self):554 df = pd.DataFrame({555 'num_legs': [2, 4, 4, 6, np.nan, np.nan],556 'num_wings': [2, 0, 0, 0, np.nan, 2]557 },558 index=['falcon', 'dog', 'cat', 'ant', 'car', 'plane'])559 with self.assertRaisesRegex(frame_base.WontImplementError,560 r"value_counts\(sort\=True\)"):561 self._run_test(lambda df: df.value_counts(sort=True), df)562 with self.assertRaisesRegex(frame_base.WontImplementError,563 r"value_counts\(sort\=True\)"):564 self._run_test(lambda df: df.num_wings.value_counts(sort=True), df)565 def test_series_getitem(self):566 s = pd.Series([x**2 for x in range(10)])567 self._run_test(lambda s: s[...], s)568 self._run_test(lambda s: s[:], s)569 self._run_test(lambda s: s[s < 10], s)570 self._run_test(lambda s: s[lambda s: s < 10], s)571 s.index = s.index.map(float)572 self._run_test(lambda s: s[1.5:6], s)573 @parameterized.expand([574 (pd.Series(range(10)), ), # unique575 (pd.Series(list(range(100)) + [0]), ), # non-unique int576 (pd.Series(list(range(100)) + [0]) / 100, ), # non-unique flt577 (pd.Series(['a', 'b', 'c', 'd']), ), # unique str578 (pd.Series(['a', 'b', 'a', 'c', 'd']), ), # non-unique str579 ])580 def test_series_is_unique(self, series):581 self._run_test(lambda s: s.is_unique, series)582 @parameterized.expand([583 (pd.Series(range(10)), ), # False584 (pd.Series([1, 2, np.nan, 3, np.nan]), ), # True585 (pd.Series(['a', 'b', 'c', 'd', 'e']), ), # False586 (pd.Series(['a', 'b', None, 'c', None]), ), # True587 ])588 def test_series_hasnans(self, series):589 self._run_test(lambda s: s.hasnans, series)590 def test_dataframe_getitem(self):591 df = pd.DataFrame({'A': [x**2 for x in range(6)], 'B': list('abcdef')})592 self._run_test(lambda df: df['A'], df)593 self._run_test(lambda df: df[['A', 'B']], df)594 self._run_test(lambda df: df[:], df)595 self._run_test(lambda df: df[df.A < 10], df)596 df.index = df.index.map(float)597 self._run_test(lambda df: df[1.5:4], df)598 def test_loc(self):599 dates = pd.date_range('1/1/2000', periods=8)600 # TODO(BEAM-11757): We do not preserve the freq attribute on a DateTime601 # index602 dates.freq = None603 df = pd.DataFrame(604 np.arange(32).reshape((8, 4)),605 index=dates,606 columns=['A', 'B', 'C', 'D'])607 self._run_test(lambda df: df.loc[:], df)608 self._run_test(lambda df: df.loc[:, 'A'], df)609 self._run_test(lambda df: df.loc[:dates[3]], df)610 self._run_test(lambda df: df.loc[df.A > 10], df)611 self._run_test(lambda df: df.loc[lambda df: df.A > 10], df)612 self._run_test(lambda df: df.C.loc[df.A > 10], df)613 self._run_test(lambda df, s: df.loc[s.loc[1:3]], df, pd.Series(dates))614 def test_append_sort(self):615 # yapf: disable616 df1 = pd.DataFrame({'int': [1, 2, 3], 'str': ['a', 'b', 'c']},617 columns=['int', 'str'],618 index=[1, 3, 5])619 df2 = pd.DataFrame({'int': [4, 5, 6], 'str': ['d', 'e', 'f']},620 columns=['str', 'int'],621 index=[2, 4, 6])622 # yapf: enable623 self._run_test(lambda df1, df2: df1.append(df2, sort=True), df1, df2)624 self._run_test(lambda df1, df2: df1.append(df2, sort=False), df1, df2)625 self._run_test(lambda df1, df2: df2.append(df1, sort=True), df1, df2)626 self._run_test(lambda df1, df2: df2.append(df1, sort=False), df1, df2)627 def test_smallest_largest(self):628 df = pd.DataFrame({'A': [1, 1, 2, 2], 'B': [2, 3, 5, 7]})629 self._run_test(lambda df: df.nlargest(1, 'A', keep='all'), df)630 self._run_test(lambda df: df.nsmallest(3, 'A', keep='all'), df)631 self._run_test(lambda df: df.nlargest(3, ['A', 'B'], keep='all'), df)632 def test_series_cov_corr(self):633 for s in [pd.Series([1, 2, 3]),634 pd.Series(range(100)),635 pd.Series([x**3 for x in range(-50, 50)])]:636 self._run_test(lambda s: s.std(), s)637 self._run_test(lambda s: s.var(), s)638 self._run_test(lambda s: s.corr(s), s)639 self._run_test(lambda s: s.corr(s + 1), s)640 self._run_test(lambda s: s.corr(s * s), s)641 self._run_test(lambda s: s.cov(s * s), s)642 self._run_test(lambda s: s.skew(), s)643 self._run_test(lambda s: s.kurtosis(), s)644 self._run_test(lambda s: s.kurt(), s)645 def test_dataframe_cov_corr(self):646 df = pd.DataFrame(np.random.randn(20, 3), columns=['a', 'b', 'c'])647 df.loc[df.index[:5], 'a'] = np.nan648 df.loc[df.index[5:10], 'b'] = np.nan649 self._run_test(lambda df: df.corr(), df)650 self._run_test(lambda df: df.cov(), df)651 self._run_test(lambda df: df.corr(min_periods=12), df)652 self._run_test(lambda df: df.cov(min_periods=12), df)653 self._run_test(lambda df: df.corrwith(df.a), df)654 self._run_test(lambda df: df[['a', 'b']].corrwith(df[['b', 'c']]), df)655 df2 = pd.DataFrame(np.random.randn(20, 3), columns=['a', 'b', 'c'])656 self._run_test(657 lambda df, df2: df.corrwith(df2, axis=1), df, df2, check_proxy=False)658 def test_corrwith_bad_axis(self):659 df = pd.DataFrame({'a': range(3), 'b': range(3, 6), 'c': range(6, 9)})660 self._run_error_test(lambda df: df.corrwith(df.a, axis=2), df)661 self._run_error_test(lambda df: df.corrwith(df, axis=5), df)662 @unittest.skipIf(PD_VERSION < (1, 2), "na_action added in pandas 1.2.0")663 def test_applymap_na_action(self):664 # Replicates a doctest for na_action which is incompatible with665 # doctest framework666 df = pd.DataFrame([[pd.NA, 2.12], [3.356, 4.567]])667 self._run_test(668 lambda df: df.applymap(lambda x: len(str(x)), na_action='ignore'),669 df,670 # TODO: generate proxy using naive type inference on fn671 check_proxy=False)672 def test_dataframe_eval_query(self):673 df = pd.DataFrame(np.random.randn(20, 3), columns=['a', 'b', 'c'])674 self._run_test(lambda df: df.eval('foo = a + b - c'), df)675 self._run_test(lambda df: df.query('a > b + c'), df)676 self._run_inplace_test(lambda df: df.eval('foo = a + b - c'), df)677 # Verify that attempting to access locals raises a useful error678 deferred_df = frame_base.DeferredFrame.wrap(679 expressions.ConstantExpression(df, df[0:0]))680 self.assertRaises(681 NotImplementedError, lambda: deferred_df.eval('foo = a + @b - c'))682 self.assertRaises(683 NotImplementedError, lambda: deferred_df.query('a > @b + c'))684 def test_index_name_assignment(self):685 df = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})686 df = df.set_index(['a', 'b'], drop=False)687 def change_index_names(df):688 df.index.names = ['A', None]689 self._run_inplace_test(change_index_names, df)690 def test_quantile(self):691 df = pd.DataFrame(692 np.array([[1, 1], [2, 10], [3, 100], [4, 100]]), columns=['a', 'b'])693 self._run_test(694 lambda df: df.quantile(0.1, axis='columns'), df, check_proxy=False)695 self._run_test(696 lambda df: df.quantile(0.1, axis='columns'), df, check_proxy=False)697 with self.assertRaisesRegex(frame_base.WontImplementError,698 r"df\.quantile\(q=0\.1, axis='columns'\)"):699 self._run_test(lambda df: df.quantile([0.1, 0.5], axis='columns'), df)700 def test_dataframe_melt(self):701 df = pd.DataFrame({702 'A': {703 0: 'a', 1: 'b', 2: 'c'704 },705 'B': {706 0: 1, 1: 3, 2: 5707 },708 'C': {709 0: 2, 1: 4, 2: 6710 }711 })712 self._run_test(713 lambda df: df.melt(id_vars=['A'], value_vars=['B'], ignore_index=False),714 df)715 self._run_test(716 lambda df: df.melt(717 id_vars=['A'], value_vars=['B', 'C'], ignore_index=False),718 df)719 self._run_test(720 lambda df: df.melt(721 id_vars=['A'],722 value_vars=['B'],723 var_name='myVarname',724 value_name='myValname',725 ignore_index=False),726 df)727 self._run_test(728 lambda df: df.melt(729 id_vars=['A'], value_vars=['B', 'C'], ignore_index=False),730 df)731 df.columns = [list('ABC'), list('DEF')]732 self._run_test(733 lambda df: df.melt(734 col_level=0, id_vars=['A'], value_vars=['B'], ignore_index=False),735 df)736 self._run_test(737 lambda df: df.melt(738 id_vars=[('A', 'D')], value_vars=[('B', 'E')], ignore_index=False),739 df)740 def test_fillna_columns(self):741 df = pd.DataFrame(742 [[np.nan, 2, np.nan, 0], [3, 4, np.nan, 1], [np.nan, np.nan, np.nan, 5],743 [np.nan, 3, np.nan, 4], [3, np.nan, np.nan, 4]],744 columns=list('ABCD'))745 self._run_test(lambda df: df.fillna(method='ffill', axis='columns'), df)746 self._run_test(747 lambda df: df.fillna(method='ffill', axis='columns', limit=1), df)748 self._run_test(749 lambda df: df.fillna(method='bfill', axis='columns', limit=1), df)750 # Intended behavior is unclear here. See751 # https://github.com/pandas-dev/pandas/issues/40989752 # self._run_test(lambda df: df.fillna(axis='columns', value=100,753 # limit=2), df)754 def test_dataframe_fillna_dataframe_as_value(self):755 df = pd.DataFrame([[np.nan, 2, np.nan, 0], [3, 4, np.nan, 1],756 [np.nan, np.nan, np.nan, 5], [np.nan, 3, np.nan, 4]],757 columns=list("ABCD"))758 df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))759 self._run_test(lambda df, df2: df.fillna(df2), df, df2)760 def test_dataframe_fillna_series_as_value(self):761 df = pd.DataFrame([[np.nan, 2, np.nan, 0], [3, 4, np.nan, 1],762 [np.nan, np.nan, np.nan, 5], [np.nan, 3, np.nan, 4]],763 columns=list("ABCD"))764 s = pd.Series(range(4), index=list("ABCE"))765 self._run_test(lambda df, s: df.fillna(s), df, s)766 def test_series_fillna_series_as_value(self):767 df = pd.DataFrame([[np.nan, 2, np.nan, 0], [3, 4, np.nan, 1],768 [np.nan, np.nan, np.nan, 5], [np.nan, 3, np.nan, 4]],769 columns=list("ABCD"))770 df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))771 self._run_test(lambda df, df2: df.A.fillna(df2.A), df, df2)772 def test_append_verify_integrity(self):773 df1 = pd.DataFrame({'A': range(10), 'B': range(10)}, index=range(10))774 df2 = pd.DataFrame({'A': range(10), 'B': range(10)}, index=range(9, 19))775 self._run_error_test(776 lambda s1,777 s2: s1.append(s2, verify_integrity=True),778 df1['A'],779 df2['A'],780 construction_time=False)781 self._run_error_test(782 lambda df1,783 df2: df1.append(df2, verify_integrity=True),784 df1,785 df2,786 construction_time=False)787 def test_categorical_groupby(self):788 df = pd.DataFrame({'A': np.arange(6), 'B': list('aabbca')})789 df['B'] = df['B'].astype(pd.CategoricalDtype(list('cab')))790 df = df.set_index('B')791 # TODO(BEAM-11190): These aggregations can be done in index partitions, but792 # it will require a little more complex logic793 self._run_test(lambda df: df.groupby(level=0).sum(), df, nonparallel=True)794 self._run_test(lambda df: df.groupby(level=0).mean(), df, nonparallel=True)795 def test_dataframe_sum_nonnumeric_raises(self):796 # Attempting a numeric aggregation with the str column present should797 # raise, and suggest the numeric_only argument798 with self.assertRaisesRegex(frame_base.WontImplementError, 'numeric_only'):799 self._run_test(lambda df: df.sum(), GROUPBY_DF)800 # numeric_only=True should work801 self._run_test(lambda df: df.sum(numeric_only=True), GROUPBY_DF)802 # projecting only numeric columns should too803 self._run_test(lambda df: df[['foo', 'bar']].sum(), GROUPBY_DF)804 def test_insert(self):805 df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})806 self._run_inplace_test(lambda df: df.insert(1, 'C', df.A * 2), df)807 self._run_inplace_test(808 lambda df: df.insert(0, 'foo', pd.Series([8], index=[1])),809 df,810 check_proxy=False)811 self._run_inplace_test(lambda df: df.insert(2, 'bar', value='q'), df)812 def test_insert_does_not_support_list_value(self):813 df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})814 with self.assertRaisesRegex(frame_base.WontImplementError,815 r"insert\(value=list\)"):816 self._run_inplace_test(lambda df: df.insert(1, 'C', [7, 8, 9]), df)817 def test_drop_duplicates(self):818 df = pd.DataFrame({819 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],820 'style': ['cup', 'cup', 'cup', 'pack', 'pack'],821 'rating': [4, 4, 3.5, 15, 5]822 })823 self._run_test(lambda df: df.drop_duplicates(keep=False), df)824 self._run_test(825 lambda df: df.drop_duplicates(subset=['brand'], keep=False), df)826 self._run_test(827 lambda df: df.drop_duplicates(subset=['brand', 'style'], keep=False),828 df)829 @parameterized.expand([830 (831 lambda base: base.from_dict({832 'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']833 }), ),834 (835 lambda base: base.from_dict({836 'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']837 },838 orient='index'), ),839 (840 lambda base: base.from_records(841 np.array([(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')],842 dtype=[('col_1', 'i4'), ('col_2', 'U1')])), ),843 ])844 def test_create_methods(self, func):845 expected = func(pd.DataFrame)846 deferred_df = func(frames.DeferredDataFrame)847 actual = expressions.Session({}).evaluate(deferred_df._expr)848 pd.testing.assert_frame_equal(actual, expected)849 def test_replace(self):850 # verify a replace() doctest case that doesn't quite work in Beam as it uses851 # the default method='pad'852 df = pd.DataFrame({'A': ['bat', 'foo', 'bait'], 'B': ['abc', 'bar', 'xyz']})853 self._run_test(854 lambda df: df.replace(855 regex={856 r'^ba.$': 'new', 'foo': 'xyz'857 }, method=None),858 df)859 def test_sample_columns(self):860 df = pd.DataFrame({861 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],862 'style': ['cup', 'cup', 'cup', 'pack', 'pack'],863 'rating': [4, 4, 3.5, 15, 5]864 })865 self._run_test(lambda df: df.sample(axis=1, n=2, random_state=1), df)866 self._run_error_test(lambda df: df.sample(axis=1, n=10, random_state=2), df)867 self._run_test(868 lambda df: df.sample(axis=1, n=10, random_state=3, replace=True), df)869 def test_cat(self):870 # Replicate the doctests from CategorigcalAccessor871 # These tests don't translate into pandas_doctests_test.py because it872 # tries to use astype("category") in Beam, which makes a non-deferred873 # column type.874 s = pd.Series(list("abbccc")).astype("category")875 self._run_test(lambda s: s.cat.rename_categories(list("cba")), s)876 self._run_test(lambda s: s.cat.reorder_categories(list("cba")), s)877 self._run_test(lambda s: s.cat.add_categories(["d", "e"]), s)878 self._run_test(lambda s: s.cat.remove_categories(["a", "c"]), s)879 self._run_test(lambda s: s.cat.set_categories(list("abcde")), s)880 self._run_test(lambda s: s.cat.as_ordered(), s)881 self._run_test(lambda s: s.cat.as_unordered(), s)882 self._run_test(lambda s: s.cat.codes, s)883 @parameterized.expand(frames.ELEMENTWISE_DATETIME_PROPERTIES)884 def test_dt_property(self, prop_name):885 # Generate a series with a lot of unique timestamps886 s = pd.Series(887 pd.date_range('1/1/2000', periods=100, freq='m') +888 pd.timedelta_range(start='0 days', end='70 days', periods=100))889 self._run_test(lambda s: getattr(s.dt, prop_name), s)890 @parameterized.expand([891 ('month_name', {}),892 ('day_name', {}),893 ('normalize', {}),894 (895 'strftime',896 {897 'date_format': '%B %d, %Y, %r'898 },899 ),900 ('tz_convert', {901 'tz': 'Europe/Berlin'902 }),903 ])904 def test_dt_method(self, op, kwargs):905 # Generate a series with a lot of unique timestamps906 s = pd.Series(907 pd.date_range(908 '1/1/2000', periods=100, freq='m', tz='America/Los_Angeles') +909 pd.timedelta_range(start='0 days', end='70 days', periods=100))910 self._run_test(lambda s: getattr(s.dt, op)(**kwargs), s)911 def test_dt_tz_localize_ambiguous_series(self):912 # This replicates a dt.tz_localize doctest:913 # s.tz_localize('CET', ambiguous=np.array([True, True, False]))914 # But using a DeferredSeries instead of a np array915 s = pd.to_datetime(916 pd.Series([917 '2018-10-28 01:20:00', '2018-10-28 02:36:00', '2018-10-28 03:46:00'918 ]))919 ambiguous = pd.Series([True, True, False], index=s.index)920 self._run_test(921 lambda s,922 ambiguous: s.dt.tz_localize('CET', ambiguous=ambiguous),923 s,924 ambiguous)925 def test_dt_tz_localize_nonexistent(self):926 # This replicates dt.tz_localize doctests that exercise `nonexistent`.927 # However they specify ambiguous='NaT' because the default,928 # ambiguous='infer', is not supported.929 s = pd.to_datetime(930 pd.Series(['2015-03-29 02:30:00', '2015-03-29 03:30:00']))931 self._run_test(932 lambda s: s.dt.tz_localize(933 'Europe/Warsaw', ambiguous='NaT', nonexistent='shift_forward'),934 s)935 self._run_test(936 lambda s: s.dt.tz_localize(937 'Europe/Warsaw', ambiguous='NaT', nonexistent='shift_backward'),938 s)939 self._run_test(940 lambda s: s.dt.tz_localize(941 'Europe/Warsaw', ambiguous='NaT', nonexistent=pd.Timedelta('1H')),942 s)943# pandas doesn't support kurtosis on GroupBys:944# https://github.com/pandas-dev/pandas/issues/40139945ALL_GROUPING_AGGREGATIONS = sorted(946 set(frames.ALL_AGGREGATIONS) - set(('kurt', 'kurtosis')))947class GroupByTest(_AbstractFrameTest):948 """Tests for DataFrame/Series GroupBy operations."""949 @parameterized.expand(ALL_GROUPING_AGGREGATIONS)950 def test_groupby_agg(self, agg_type):951 if agg_type == 'describe' and PD_VERSION < (1, 2):952 self.skipTest(953 "BEAM-12366: proxy generation of DataFrameGroupBy.describe "954 "fails in pandas < 1.2")955 self._run_test(956 lambda df: df.groupby('group').agg(agg_type),957 GROUPBY_DF,958 check_proxy=False)959 @parameterized.expand(ALL_GROUPING_AGGREGATIONS)960 def test_groupby_with_filter(self, agg_type):961 if agg_type == 'describe' and PD_VERSION < (1, 2):962 self.skipTest(963 "BEAM-12366: proxy generation of DataFrameGroupBy.describe "964 "fails in pandas < 1.2")965 self._run_test(966 lambda df: getattr(df[df.foo > 30].groupby('group'), agg_type)(),967 GROUPBY_DF,968 check_proxy=False)969 @parameterized.expand(ALL_GROUPING_AGGREGATIONS)970 def test_groupby(self, agg_type):971 if agg_type == 'describe' and PD_VERSION < (1, 2):972 self.skipTest(973 "BEAM-12366: proxy generation of DataFrameGroupBy.describe "974 "fails in pandas < 1.2")975 self._run_test(976 lambda df: getattr(df.groupby('group'), agg_type)(),977 GROUPBY_DF,978 check_proxy=False)979 @parameterized.expand(ALL_GROUPING_AGGREGATIONS)980 def test_groupby_series(self, agg_type):981 if agg_type == 'describe' and PD_VERSION < (1, 2):982 self.skipTest(983 "BEAM-12366: proxy generation of DataFrameGroupBy.describe "984 "fails in pandas < 1.2")985 self._run_test(986 lambda df: getattr(df[df.foo > 40].groupby(df.group), agg_type)(),987 GROUPBY_DF,988 check_proxy=False)989 def test_groupby_user_guide(self):990 # Example from https://pandas.pydata.org/docs/user_guide/groupby.html991 arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],992 ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]993 index = pd.MultiIndex.from_arrays(arrays, names=['first', 'second'])994 df = pd.DataFrame({995 'A': [1, 1, 1, 1, 2, 2, 3, 3], 'B': np.arange(8)996 },997 index=index)998 self._run_test(lambda df: df.groupby(['second', 'A']).sum(), df)999 @parameterized.expand(ALL_GROUPING_AGGREGATIONS)1000 def test_groupby_project_series(self, agg_type):1001 df = GROUPBY_DF1002 if agg_type == 'describe':1003 self.skipTest(1004 "BEAM-12366: proxy generation of SeriesGroupBy.describe "1005 "fails")1006 if agg_type in ('corr', 'cov'):1007 self.skipTest(1008 "BEAM-12367: SeriesGroupBy.{corr, cov} do not raise the "1009 "expected error.")1010 self._run_test(lambda df: getattr(df.groupby('group').foo, agg_type)(), df)1011 self._run_test(lambda df: getattr(df.groupby('group').bar, agg_type)(), df)1012 self._run_test(1013 lambda df: getattr(df.groupby('group')['foo'], agg_type)(), df)1014 self._run_test(1015 lambda df: getattr(df.groupby('group')['bar'], agg_type)(), df)1016 @parameterized.expand(ALL_GROUPING_AGGREGATIONS)1017 def test_groupby_project_dataframe(self, agg_type):1018 if agg_type == 'describe' and PD_VERSION < (1, 2):1019 self.skipTest(1020 "BEAM-12366: proxy generation of DataFrameGroupBy.describe "1021 "fails in pandas < 1.2")1022 self._run_test(1023 lambda df: getattr(df.groupby('group')[['bar', 'baz']], agg_type)(),1024 GROUPBY_DF,1025 check_proxy=False)1026 def test_groupby_errors_bad_projection(self):1027 df = GROUPBY_DF1028 # non-existent projection column1029 self._run_error_test(1030 lambda df: df.groupby('group')[['bar', 'baz']].bar.median(), df)1031 self._run_error_test(lambda df: df.groupby('group')[['bad']].median(), df)1032 self._run_error_test(lambda df: df.groupby('group').bad.median(), df)1033 self._run_error_test(1034 lambda df: df.groupby('group')[['bar', 'baz']].bar.sum(), df)1035 self._run_error_test(lambda df: df.groupby('group')[['bat']].sum(), df)1036 self._run_error_test(lambda df: df.groupby('group').bat.sum(), df)1037 def test_groupby_errors_non_existent_label(self):1038 df = GROUPBY_DF1039 # non-existent grouping label1040 self._run_error_test(1041 lambda df: df.groupby(['really_bad', 'foo', 'bad']).foo.sum(), df)1042 self._run_error_test(lambda df: df.groupby('bad').foo.sum(), df)1043 def test_groupby_callable(self):1044 df = GROUPBY_DF1045 self._run_test(lambda df: df.groupby(lambda x: x % 2).foo.sum(), df)1046 self._run_test(lambda df: df.groupby(lambda x: x % 5).median(), df)1047 def test_groupby_apply(self):1048 df = GROUPBY_DF1049 def median_sum_fn(x):1050 return (x.foo + x.bar).median()1051 # Note this is the same as DataFrameGroupBy.describe. Using it here is1052 # just a convenient way to test apply() with a user fn that returns a Series1053 describe = lambda df: df.describe()1054 self._run_test(lambda df: df.groupby('group').foo.apply(describe), df)1055 self._run_test(1056 lambda df: df.groupby('group')[['foo', 'bar']].apply(describe), df)1057 self._run_test(lambda df: df.groupby('group').apply(median_sum_fn), df)1058 self._run_test(1059 lambda df: df.set_index('group').foo.groupby(level=0).apply(describe),1060 df)1061 self._run_test(lambda df: df.groupby(level=0).apply(median_sum_fn), df)1062 self._run_test(lambda df: df.groupby(lambda x: x % 3).apply(describe), df)1063 self._run_test(1064 lambda df: df.bar.groupby(lambda x: x % 3).apply(describe), df)1065 self._run_test(1066 lambda df: df.set_index(['str', 'group', 'bool']).groupby(1067 level='group').apply(median_sum_fn),1068 df)1069 def test_groupby_apply_preserves_column_order(self):1070 df = GROUPBY_DF1071 self._run_test(1072 lambda df: df[['foo', 'group', 'bar']].groupby('group').apply(1073 lambda x: x),1074 df)1075 def test_groupby_transform(self):1076 df = pd.DataFrame({1077 "Date": [1078 "2015-05-08",1079 "2015-05-07",1080 "2015-05-06",1081 "2015-05-05",1082 "2015-05-08",1083 "2015-05-07",1084 "2015-05-06",1085 "2015-05-05"1086 ],1087 "Data": [5, 8, 6, 1, 50, 100, 60, 120],1088 })1089 self._run_test(lambda df: df.groupby('Date')['Data'].transform(np.sum), df)1090 self._run_test(1091 lambda df: df.groupby('Date')['Data'].transform(1092 lambda x: (x - x.mean()) / x.std()),1093 df)1094 def test_groupby_apply_modified_index(self):1095 df = GROUPBY_DF1096 # If apply fn modifies the index then the output will include the grouped1097 # index1098 self._run_test(1099 lambda df: df.groupby('group').apply(1100 lambda x: x[x.foo > x.foo.median()]),1101 df)1102 @unittest.skip('BEAM-11710')1103 def test_groupby_aggregate_grouped_column(self):1104 df = pd.DataFrame({1105 'group': ['a' if i % 5 == 0 or i % 3 == 0 else 'b' for i in range(100)],1106 'foo': [None if i % 11 == 0 else i for i in range(100)],1107 'bar': [None if i % 7 == 0 else 99 - i for i in range(100)],1108 'baz': [None if i % 13 == 0 else i * 2 for i in range(100)],1109 })1110 self._run_test(lambda df: df.groupby('group').group.count(), df)1111 self._run_test(lambda df: df.groupby('group')[['group', 'bar']].count(), df)1112 self._run_test(1113 lambda df: df.groupby('group')[['group', 'bar']].apply(1114 lambda x: x.describe()),1115 df)1116 @parameterized.expand((x, ) for x in [1117 0,1118 [1],1119 3,1120 [0, 3],1121 [2, 1],1122 ['foo', 0],1123 [1, 'str'],1124 [3, 0, 2, 1],1125 ])1126 def test_groupby_level_agg(self, level):1127 df = GROUPBY_DF.set_index(['group', 'foo', 'bar', 'str'], drop=False)1128 self._run_test(lambda df: df.groupby(level=level).bar.max(), df)1129 self._run_test(1130 lambda df: df.groupby(level=level).sum(numeric_only=True), df)1131 self._run_test(1132 lambda df: df.groupby(level=level).apply(1133 lambda x: (x.foo + x.bar).median()),1134 df)1135 @unittest.skipIf(PD_VERSION < (1, 1), "drop_na added in pandas 1.1.0")1136 def test_groupby_count_na(self):1137 # Verify we can do a groupby.count() that doesn't drop NaN values1138 self._run_test(1139 lambda df: df.groupby('foo', dropna=True).bar.count(), GROUPBY_DF)1140 self._run_test(1141 lambda df: df.groupby('foo', dropna=False).bar.count(), GROUPBY_DF)1142 def test_groupby_sum_min_count(self):1143 df = pd.DataFrame({1144 'good': [1, 2, 3, np.nan],1145 'bad': [np.nan, np.nan, np.nan, 4],1146 'group': ['a', 'b', 'a', 'b']1147 })1148 self._run_test(lambda df: df.groupby('group').sum(min_count=2), df)1149 def test_groupby_dtypes(self):1150 self._run_test(1151 lambda df: df.groupby('group').dtypes, GROUPBY_DF, check_proxy=False)1152 self._run_test(1153 lambda df: df.groupby(level=0).dtypes, GROUPBY_DF, check_proxy=False)1154 @parameterized.expand(ALL_GROUPING_AGGREGATIONS)1155 def test_dataframe_groupby_series(self, agg_type):1156 if agg_type == 'describe' and PD_VERSION < (1, 2):1157 self.skipTest(1158 "BEAM-12366: proxy generation of DataFrameGroupBy.describe "1159 "fails in pandas < 1.2")1160 self._run_test(1161 lambda df: df[df.foo > 40].groupby(df.group).agg(agg_type),1162 GROUPBY_DF,1163 check_proxy=False)1164 self._run_test(1165 lambda df: df[df.foo > 40].groupby(df.foo % 3).agg(agg_type),1166 GROUPBY_DF,1167 check_proxy=False)1168 @parameterized.expand(ALL_GROUPING_AGGREGATIONS)1169 def test_series_groupby_series(self, agg_type):1170 if agg_type == 'describe':1171 self.skipTest(1172 "BEAM-12366: proxy generation of SeriesGroupBy.describe "1173 "fails")1174 if agg_type in ('corr', 'cov'):1175 self.skipTest(1176 "BEAM-12367: SeriesGroupBy.{corr, cov} do not raise the "1177 "expected error.")1178 self._run_test(1179 lambda df: df[df.foo < 40].bar.groupby(df.group).agg(agg_type),1180 GROUPBY_DF)1181 self._run_test(1182 lambda df: df[df.foo < 40].bar.groupby(df.foo % 3).agg(agg_type),1183 GROUPBY_DF)1184 def test_groupby_series_apply(self):1185 df = GROUPBY_DF1186 def median_sum_fn(x):1187 return (x.foo + x.bar).median()1188 # Note this is the same as DataFrameGroupBy.describe. Using it here is1189 # just a convenient way to test apply() with a user fn that returns a Series1190 describe = lambda df: df.describe()1191 self._run_test(lambda df: df.groupby(df.group).foo.apply(describe), df)1192 self._run_test(1193 lambda df: df.groupby(df.group)[['foo', 'bar']].apply(describe), df)1194 self._run_test(lambda df: df.groupby(df.group).apply(median_sum_fn), df)1195 def test_groupby_multiindex_keep_nans(self):1196 # Due to https://github.com/pandas-dev/pandas/issues/364701197 # groupby(dropna=False) doesn't work with multiple columns1198 with self.assertRaisesRegex(NotImplementedError, "BEAM-12495"):1199 self._run_test(1200 lambda df: df.groupby(['foo', 'bar'], dropna=False).sum(), GROUPBY_DF)1201class AggregationTest(_AbstractFrameTest):1202 """Tests for global aggregation methods on DataFrame/Series."""1203 # corr, cov on Series require an other argument1204 @parameterized.expand(1205 sorted(set(frames.ALL_AGGREGATIONS) - set(['corr', 'cov'])))1206 def test_series_agg(self, agg_method):1207 s = pd.Series(list(range(16)))1208 nonparallel = agg_method in (1209 'quantile', 'mean', 'describe', 'median', 'sem', 'mad')1210 # TODO(BEAM-12379): max and min produce the wrong proxy1211 check_proxy = agg_method not in ('max', 'min')1212 self._run_test(1213 lambda s: s.agg(agg_method),1214 s,1215 nonparallel=nonparallel,1216 check_proxy=check_proxy)1217 # corr, cov on Series require an other argument1218 # Series.size is a property1219 @parameterized.expand(1220 sorted(set(frames.ALL_AGGREGATIONS) - set(['corr', 'cov', 'size'])))1221 def test_series_agg_method(self, agg_method):1222 s = pd.Series(list(range(16)))1223 nonparallel = agg_method in (1224 'quantile', 'mean', 'describe', 'median', 'sem', 'mad')1225 # TODO(BEAM-12379): max and min produce the wrong proxy1226 check_proxy = agg_method not in ('max', 'min')1227 self._run_test(1228 lambda s: getattr(s, agg_method)(),1229 s,1230 nonparallel=nonparallel,1231 check_proxy=check_proxy)1232 @parameterized.expand(frames.ALL_AGGREGATIONS)1233 def test_dataframe_agg(self, agg_method):1234 df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [2, 3, 5, 7]})1235 nonparallel = agg_method in (1236 'quantile', 'mean', 'describe', 'median', 'sem', 'mad')1237 # TODO(BEAM-12379): max and min produce the wrong proxy1238 check_proxy = agg_method not in ('max', 'min')1239 self._run_test(1240 lambda df: df.agg(agg_method),1241 df,1242 nonparallel=nonparallel,1243 check_proxy=check_proxy)1244 # DataFrame.size is a property1245 @parameterized.expand(sorted(set(frames.ALL_AGGREGATIONS) - set(['size'])))1246 def test_dataframe_agg_method(self, agg_method):1247 df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [2, 3, 5, 7]})1248 nonparallel = agg_method in (1249 'quantile', 'mean', 'describe', 'median', 'sem', 'mad')1250 # TODO(BEAM-12379): max and min produce the wrong proxy1251 check_proxy = agg_method not in ('max', 'min')1252 self._run_test(1253 lambda df: getattr(df, agg_method)(),1254 df,1255 nonparallel=nonparallel,1256 check_proxy=check_proxy)1257 def test_series_agg_modes(self):1258 s = pd.Series(list(range(16)))1259 self._run_test(lambda s: s.agg('sum'), s)1260 self._run_test(lambda s: s.agg(['sum']), s)1261 self._run_test(lambda s: s.agg(['sum', 'mean']), s, nonparallel=True)1262 self._run_test(lambda s: s.agg(['mean']), s, nonparallel=True)1263 self._run_test(lambda s: s.agg('mean'), s, nonparallel=True)1264 def test_dataframe_agg_modes(self):1265 df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [2, 3, 5, 7]})1266 self._run_test(lambda df: df.agg('sum'), df)1267 self._run_test(lambda df: df.agg(['sum', 'mean']), df, nonparallel=True)1268 self._run_test(lambda df: df.agg({'A': 'sum', 'B': 'sum'}), df)1269 self._run_test(1270 lambda df: df.agg({1271 'A': 'sum', 'B': 'mean'1272 }), df, nonparallel=True)1273 self._run_test(1274 lambda df: df.agg({'A': ['sum', 'mean']}), df, nonparallel=True)1275 self._run_test(1276 lambda df: df.agg({1277 'A': ['sum', 'mean'], 'B': 'min'1278 }),1279 df,1280 nonparallel=True)1281 def test_series_agg_level(self):1282 self._run_test(1283 lambda df: df.set_index(['group', 'foo']).bar.count(level=0),1284 GROUPBY_DF)1285 self._run_test(1286 lambda df: df.set_index(['group', 'foo']).bar.max(level=0), GROUPBY_DF)1287 self._run_test(1288 lambda df: df.set_index(['group', 'foo']).bar.median(level=0),1289 GROUPBY_DF)1290 self._run_test(1291 lambda df: df.set_index(['foo', 'group']).bar.count(level=1),1292 GROUPBY_DF)1293 self._run_test(1294 lambda df: df.set_index(['group', 'foo']).bar.max(level=1), GROUPBY_DF)1295 self._run_test(1296 lambda df: df.set_index(['group', 'foo']).bar.max(level='foo'),1297 GROUPBY_DF)1298 self._run_test(1299 lambda df: df.set_index(['group', 'foo']).bar.median(level=1),1300 GROUPBY_DF)1301 def test_dataframe_agg_level(self):1302 self._run_test(1303 lambda df: df.set_index(['group', 'foo']).count(level=0), GROUPBY_DF)1304 self._run_test(1305 lambda df: df.set_index(['group', 'foo']).max(1306 level=0, numeric_only=False),1307 GROUPBY_DF,1308 check_proxy=False)1309 # pandas implementation doesn't respect numeric_only argument here1310 # (https://github.com/pandas-dev/pandas/issues/40788), it1311 # always acts as if numeric_only=True. Our implmentation respects it so we1312 # need to make it explicit.1313 self._run_test(1314 lambda df: df.set_index(['group', 'foo']).sum(1315 level=0, numeric_only=True),1316 GROUPBY_DF)1317 self._run_test(1318 lambda df: df.set_index(['group', 'foo'])[['bar']].count(level=1),1319 GROUPBY_DF)1320 self._run_test(1321 lambda df: df.set_index(['group', 'foo']).count(level=1), GROUPBY_DF)1322 self._run_test(1323 lambda df: df.set_index(['group', 'foo']).max(1324 level=1, numeric_only=False),1325 GROUPBY_DF,1326 check_proxy=False)1327 # sum with str columns is order-sensitive1328 self._run_test(1329 lambda df: df.set_index(['group', 'foo']).sum(1330 level=1, numeric_only=True),1331 GROUPBY_DF)1332 self._run_test(1333 lambda df: df.set_index(['group', 'foo']).median(1334 level=0, numeric_only=True),1335 GROUPBY_DF)1336 self._run_test(1337 lambda df: df.drop('str', axis=1).set_index(['foo', 'group']).median(1338 level=1, numeric_only=True),1339 GROUPBY_DF)1340 def test_series_agg_multifunc_level(self):1341 # level= is ignored for multiple agg fns1342 self._run_test(1343 lambda df: df.set_index(['group', 'foo']).bar.agg(['min', 'max'],1344 level=0),1345 GROUPBY_DF)1346 def test_dataframe_agg_multifunc_level(self):1347 # level= is ignored for multiple agg fns1348 self._run_test(1349 lambda df: df.set_index(['group', 'foo']).agg(['min', 'max'], level=0),1350 GROUPBY_DF,1351 check_proxy=False)1352 @parameterized.expand([(True, ), (False, )])1353 @unittest.skipIf(1354 PD_VERSION < (1, 2),1355 "pandas 1.1.0 produces different dtypes for these examples")1356 def test_dataframe_agg_numeric_only(self, numeric_only):1357 # Note other aggregation functions can fail on this input with1358 # numeric_only={False,None}. These are the only ones that actually work for1359 # the string inputs.1360 self._run_test(1361 lambda df: df.max(numeric_only=numeric_only),1362 GROUPBY_DF,1363 check_proxy=False)1364 self._run_test(1365 lambda df: df.min(numeric_only=numeric_only),1366 GROUPBY_DF,1367 check_proxy=False)1368 @unittest.skip(1369 "pandas implementation doesn't respect numeric_only= with "1370 "level= (https://github.com/pandas-dev/pandas/issues/40788)")1371 def test_dataframe_agg_level_numeric_only(self):1372 self._run_test(1373 lambda df: df.set_index('foo').sum(level=0, numeric_only=True),1374 GROUPBY_DF)1375 self._run_test(1376 lambda df: df.set_index('foo').max(level=0, numeric_only=True),1377 GROUPBY_DF)1378 self._run_test(1379 lambda df: df.set_index('foo').mean(level=0, numeric_only=True),1380 GROUPBY_DF)1381 self._run_test(1382 lambda df: df.set_index('foo').median(level=0, numeric_only=True),1383 GROUPBY_DF)1384 def test_dataframe_agg_bool_only(self):1385 df = pd.DataFrame({1386 'all': [True for i in range(10)],1387 'any': [i % 3 == 0 for i in range(10)],1388 'int': range(10)1389 })1390 self._run_test(lambda df: df.all(), df)1391 self._run_test(lambda df: df.any(), df)1392 self._run_test(lambda df: df.all(bool_only=True), df)1393 self._run_test(lambda df: df.any(bool_only=True), df)1394 @unittest.skip(1395 "pandas doesn't implement bool_only= with level= "1396 "(https://github.com/pandas-dev/pandas/blob/"1397 "v1.2.3/pandas/core/generic.py#L10573)")1398 def test_dataframe_agg_level_bool_only(self):1399 df = pd.DataFrame({1400 'all': [True for i in range(10)],1401 'any': [i % 3 == 0 for i in range(10)],1402 'int': range(10)1403 })1404 self._run_test(lambda df: df.set_index('int', drop=False).all(level=0), df)1405 self._run_test(lambda df: df.set_index('int', drop=False).any(level=0), df)1406 self._run_test(1407 lambda df: df.set_index('int', drop=False).all(level=0, bool_only=True),1408 df)1409 self._run_test(1410 lambda df: df.set_index('int', drop=False).any(level=0, bool_only=True),1411 df)1412 def test_series_agg_np_size(self):1413 self._run_test(1414 lambda df: df.set_index(['group', 'foo']).agg(np.size),1415 GROUPBY_DF,1416 check_proxy=False)1417 def test_df_agg_invalid_kwarg_raises(self):1418 self._run_error_test(lambda df: df.agg('mean', bool_only=True), GROUPBY_DF)1419 self._run_error_test(1420 lambda df: df.agg('any', numeric_only=True), GROUPBY_DF)1421 self._run_error_test(1422 lambda df: df.agg('median', min_count=3, numeric_only=True), GROUPBY_DF)1423 def test_series_agg_method_invalid_kwarg_raises(self):1424 self._run_error_test(lambda df: df.foo.median(min_count=3), GROUPBY_DF)1425 self._run_error_test(1426 lambda df: df.foo.agg('median', min_count=3), GROUPBY_DF)1427 @unittest.skipIf(1428 PD_VERSION < (1, 3),1429 (1430 "DataFrame.agg raises a different exception from the "1431 "aggregation methods. Fixed in "1432 "https://github.com/pandas-dev/pandas/pull/40543."))1433 def test_df_agg_method_invalid_kwarg_raises(self):1434 self._run_error_test(lambda df: df.mean(bool_only=True), GROUPBY_DF)1435 self._run_error_test(lambda df: df.any(numeric_only=True), GROUPBY_DF)1436 self._run_error_test(1437 lambda df: df.median(min_count=3, numeric_only=True), GROUPBY_DF)1438 def test_agg_min_count(self):1439 df = pd.DataFrame({1440 'good': [1, 2, 3, np.nan],1441 'bad': [np.nan, np.nan, np.nan, 4],1442 },1443 index=['a', 'b', 'a', 'b'])1444 self._run_test(lambda df: df.sum(level=0, min_count=2), df)1445 self._run_test(lambda df: df.sum(min_count=3), df, nonparallel=True)1446 self._run_test(lambda df: df.sum(min_count=1), df, nonparallel=True)1447 self._run_test(lambda df: df.good.sum(min_count=2), df, nonparallel=True)1448 self._run_test(lambda df: df.bad.sum(min_count=2), df, nonparallel=True)1449 def test_series_agg_std(self):1450 s = pd.Series(range(10))1451 self._run_test(lambda s: s.agg('std'), s)1452 self._run_test(lambda s: s.agg('var'), s)1453 self._run_test(lambda s: s.agg(['std', 'sum']), s)1454 self._run_test(lambda s: s.agg(['var']), s)1455 def test_std_all_na(self):1456 s = pd.Series([np.nan] * 10)1457 self._run_test(lambda s: s.agg('std'), s)1458 self._run_test(lambda s: s.std(), s)1459 def test_std_mostly_na_with_ddof(self):1460 df = pd.DataFrame({1461 'one': [i if i % 8 == 0 else np.nan for i in range(8)],1462 'two': [i if i % 4 == 0 else np.nan for i in range(8)],1463 'three': [i if i % 2 == 0 else np.nan for i in range(8)],1464 },1465 index=pd.MultiIndex.from_arrays(1466 [list(range(8)), list(reversed(range(8)))],1467 names=['forward', None]))1468 self._run_test(lambda df: df.std(), df) # ddof=11469 self._run_test(lambda df: df.std(ddof=0), df)1470 self._run_test(lambda df: df.std(ddof=2), df)1471 self._run_test(lambda df: df.std(ddof=3), df)1472 self._run_test(lambda df: df.std(ddof=4), df)1473 def test_dataframe_std(self):1474 self._run_test(lambda df: df.std(numeric_only=True), GROUPBY_DF)1475 self._run_test(lambda df: df.var(numeric_only=True), GROUPBY_DF)1476 def test_dataframe_mode(self):1477 self._run_test(1478 lambda df: df.mode(), GROUPBY_DF, nonparallel=True, check_proxy=False)1479 self._run_test(1480 lambda df: df.mode(numeric_only=True),1481 GROUPBY_DF,1482 nonparallel=True,1483 check_proxy=False)1484 self._run_test(1485 lambda df: df.mode(dropna=True, numeric_only=True),1486 GROUPBY_DF,1487 nonparallel=True,1488 check_proxy=False)1489 def test_series_mode(self):1490 self._run_test(lambda df: df.foo.mode(), GROUPBY_DF, nonparallel=True)1491 self._run_test(1492 lambda df: df.baz.mode(dropna=True), GROUPBY_DF, nonparallel=True)1493class BeamSpecificTest(unittest.TestCase):1494 """Tests for functionality that's specific to the Beam DataFrame API.1495 These features don't exist in pandas so we must verify them independently."""1496 def assert_frame_data_equivalent(self, actual, expected):1497 """Verify that actual is the same as expected, ignoring the index and order1498 of the data."""1499 def sort_and_drop_index(df):1500 if isinstance(df, pd.Series):1501 df = df.sort_values()1502 elif isinstance(df, pd.DataFrame):1503 df = df.sort_values(by=list(df.columns))1504 return df.reset_index(drop=True)1505 actual = sort_and_drop_index(actual)1506 expected = sort_and_drop_index(expected)1507 if isinstance(expected, pd.Series):1508 pd.testing.assert_series_equal(actual, expected)1509 elif isinstance(expected, pd.DataFrame):1510 pd.testing.assert_frame_equal(actual, expected)1511 def _evaluate(self, func, *args, distributed=True):1512 deferred_args = [1513 frame_base.DeferredFrame.wrap(1514 expressions.ConstantExpression(arg, arg[0:0])) for arg in args1515 ]1516 session_type = (1517 expressions.PartitioningSession if distributed else expressions.Session)1518 return session_type({}).evaluate(func(*deferred_args)._expr)1519 def test_drop_duplicates_keep_any(self):1520 df = pd.DataFrame({1521 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],1522 'style': ['cup', 'cup', 'cup', 'pack', 'pack'],1523 'rating': [4, 4, 3.5, 15, 5]1524 })1525 result = self._evaluate(lambda df: df.drop_duplicates(keep='any'), df)1526 # Verify that the result is the same as conventional drop_duplicates1527 self.assert_frame_data_equivalent(result, df.drop_duplicates())1528 def test_drop_duplicates_keep_any_subset(self):1529 df = pd.DataFrame({1530 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],1531 'style': ['cup', 'cup', 'cup', 'pack', 'pack'],1532 'rating': [4, 4, 3.5, 15, 5]1533 })1534 result = self._evaluate(1535 lambda df: df.drop_duplicates(keep='any', subset=['brand']), df)1536 self.assertTrue(result.brand.unique)1537 self.assert_frame_data_equivalent(1538 result.brand, df.drop_duplicates(subset=['brand']).brand)1539 def test_series_drop_duplicates_keep_any(self):1540 df = pd.DataFrame({1541 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],1542 'style': ['cup', 'cup', 'cup', 'pack', 'pack'],1543 'rating': [4, 4, 3.5, 15, 5]1544 })1545 result = self._evaluate(lambda df: df.brand.drop_duplicates(keep='any'), df)1546 self.assert_frame_data_equivalent(result, df.brand.drop_duplicates())1547 def test_duplicated_keep_any(self):1548 df = pd.DataFrame({1549 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],1550 'style': ['cup', 'cup', 'cup', 'pack', 'pack'],1551 'rating': [4, 4, 3.5, 15, 5]1552 })1553 result = self._evaluate(lambda df: df.duplicated(keep='any'), df)1554 # Verify that the result is the same as conventional duplicated1555 self.assert_frame_data_equivalent(result, df.duplicated())1556 def test_nsmallest_any(self):1557 df = pd.DataFrame({1558 'population': [1559 59000000,1560 65000000,1561 434000,1562 434000,1563 434000,1564 337000,1565 337000,1566 11300,1567 113001568 ],1569 'GDP': [1937894, 2583560, 12011, 4520, 12128, 17036, 182, 38, 311],1570 'alpha-2': ["IT", "FR", "MT", "MV", "BN", "IS", "NR", "TV", "AI"]1571 },1572 index=[1573 "Italy",1574 "France",1575 "Malta",1576 "Maldives",1577 "Brunei",1578 "Iceland",1579 "Nauru",1580 "Tuvalu",1581 "Anguilla"1582 ])1583 result = self._evaluate(1584 lambda df: df.population.nsmallest(3, keep='any'), df)1585 # keep='any' should produce the same result as keep='first',1586 # but not necessarily with the same index1587 self.assert_frame_data_equivalent(result, df.population.nsmallest(3))1588 def test_nlargest_any(self):1589 df = pd.DataFrame({1590 'population': [1591 59000000,1592 65000000,1593 434000,1594 434000,1595 434000,1596 337000,1597 337000,1598 11300,1599 113001600 ],1601 'GDP': [1937894, 2583560, 12011, 4520, 12128, 17036, 182, 38, 311],1602 'alpha-2': ["IT", "FR", "MT", "MV", "BN", "IS", "NR", "TV", "AI"]1603 },1604 index=[1605 "Italy",1606 "France",1607 "Malta",1608 "Maldives",1609 "Brunei",1610 "Iceland",1611 "Nauru",1612 "Tuvalu",1613 "Anguilla"1614 ])1615 result = self._evaluate(1616 lambda df: df.population.nlargest(3, keep='any'), df)1617 # keep='any' should produce the same result as keep='first',1618 # but not necessarily with the same index1619 self.assert_frame_data_equivalent(result, df.population.nlargest(3))1620 def test_sample(self):1621 df = pd.DataFrame({1622 'population': [1623 59000000,1624 65000000,1625 434000,1626 434000,1627 434000,1628 337000,1629 337000,1630 11300,1631 113001632 ],1633 'GDP': [1937894, 2583560, 12011, 4520, 12128, 17036, 182, 38, 311],1634 'alpha-2': ["IT", "FR", "MT", "MV", "BN", "IS", "NR", "TV", "AI"]1635 },1636 index=[1637 "Italy",1638 "France",1639 "Malta",1640 "Maldives",1641 "Brunei",1642 "Iceland",1643 "Nauru",1644 "Tuvalu",1645 "Anguilla"1646 ])1647 result = self._evaluate(lambda df: df.sample(n=3), df)1648 self.assertEqual(len(result), 3)1649 series_result = self._evaluate(lambda df: df.GDP.sample(n=3), df)1650 self.assertEqual(len(series_result), 3)1651 self.assertEqual(series_result.name, "GDP")1652 def test_sample_with_weights(self):1653 df = pd.DataFrame({1654 'population': [1655 59000000,1656 65000000,1657 434000,1658 434000,1659 434000,1660 337000,1661 337000,1662 11300,1663 113001664 ],1665 'GDP': [1937894, 2583560, 12011, 4520, 12128, 17036, 182, 38, 311],1666 'alpha-2': ["IT", "FR", "MT", "MV", "BN", "IS", "NR", "TV", "AI"]1667 },1668 index=[1669 "Italy",1670 "France",1671 "Malta",1672 "Maldives",1673 "Brunei",1674 "Iceland",1675 "Nauru",1676 "Tuvalu",1677 "Anguilla"1678 ])1679 weights = pd.Series([0, 0, 0, 0, 0, 0, 0, 1, 1], index=df.index)1680 result = self._evaluate(1681 lambda df, weights: df.sample(n=2, weights=weights), df, weights)1682 self.assertEqual(len(result), 2)1683 self.assertEqual(set(result.index), set(["Tuvalu", "Anguilla"]))1684 series_result = self._evaluate(1685 lambda df, weights: df.GDP.sample(n=2, weights=weights), df, weights)1686 self.assertEqual(len(series_result), 2)1687 self.assertEqual(series_result.name, "GDP")1688 self.assertEqual(set(series_result.index), set(["Tuvalu", "Anguilla"]))1689 def test_sample_with_missing_weights(self):1690 df = pd.DataFrame({1691 'population': [1692 59000000,1693 65000000,1694 434000,1695 434000,1696 434000,1697 337000,1698 337000,1699 11300,1700 113001701 ],1702 'GDP': [1937894, 2583560, 12011, 4520, 12128, 17036, 182, 38, 311],1703 'alpha-2': ["IT", "FR", "MT", "MV", "BN", "IS", "NR", "TV", "AI"]1704 },1705 index=[1706 "Italy",1707 "France",1708 "Malta",1709 "Maldives",1710 "Brunei",1711 "Iceland",1712 "Nauru",1713 "Tuvalu",1714 "Anguilla"1715 ])1716 # Missing weights are treated as 01717 weights = pd.Series([.1, .01, np.nan, 0],1718 index=["Nauru", "Iceland", "Anguilla", "Italy"])1719 result = self._evaluate(1720 lambda df, weights: df.sample(n=2, weights=weights), df, weights)1721 self.assertEqual(len(result), 2)1722 self.assertEqual(set(result.index), set(["Nauru", "Iceland"]))1723 series_result = self._evaluate(1724 lambda df, weights: df.GDP.sample(n=2, weights=weights), df, weights)1725 self.assertEqual(len(series_result), 2)1726 self.assertEqual(series_result.name, "GDP")1727 self.assertEqual(set(series_result.index), set(["Nauru", "Iceland"]))1728 def test_sample_with_weights_distribution(self):1729 target_prob = 0.251730 num_samples = 1001731 num_targets = 2001732 num_other_elements = 100001733 target_weight = target_prob / num_targets1734 other_weight = (1 - target_prob) / num_other_elements1735 self.assertTrue(target_weight > other_weight * 10, "weights too close")1736 result = self._evaluate(1737 lambda s,1738 weights: s.sample(n=num_samples, weights=weights).sum(),1739 # The first elements are 1, the rest are all 0. This means that when1740 # we sum all the sampled elements (above), the result should be the1741 # number of times the first elements (aka targets) were sampled.1742 pd.Series([1] * num_targets + [0] * num_other_elements),1743 pd.Series([target_weight] * num_targets +1744 [other_weight] * num_other_elements))1745 # With the above constants, the probability of violating this invariant1746 # (as computed using the Bernoulli distribution) is about 0.0012%.1747 expected = num_samples * target_prob1748 self.assertTrue(expected / 3 < result < expected * 2, (expected, result))1749class AllowNonParallelTest(unittest.TestCase):1750 def _use_non_parallel_operation(self):1751 _ = frame_base.DeferredFrame.wrap(1752 expressions.PlaceholderExpression(pd.Series([1, 2, 3]))).replace(1753 'a', 'b', limit=1)1754 def test_disallow_non_parallel(self):1755 with self.assertRaises(expressions.NonParallelOperation):1756 self._use_non_parallel_operation()1757 def test_allow_non_parallel_in_context(self):1758 with beam.dataframe.allow_non_parallel_operations():1759 self._use_non_parallel_operation()1760 def test_allow_non_parallel_nesting(self):1761 # disallowed1762 with beam.dataframe.allow_non_parallel_operations():1763 # allowed1764 self._use_non_parallel_operation()1765 with beam.dataframe.allow_non_parallel_operations(False):1766 # disallowed again1767 with self.assertRaises(expressions.NonParallelOperation):1768 self._use_non_parallel_operation()1769 # allowed1770 self._use_non_parallel_operation()1771 # disallowed1772 with self.assertRaises(expressions.NonParallelOperation):1773 self._use_non_parallel_operation()1774class ConstructionTimeTest(unittest.TestCase):1775 """Tests for operations that can be executed eagerly."""1776 DF = pd.DataFrame({1777 'str_col': ['foo', 'bar'] * 3,1778 'int_col': [1, 2] * 3,1779 'flt_col': [1.1, 2.2] * 3,1780 'cat_col': pd.Series(list('aabbca'), dtype="category"),1781 'datetime_col': pd.Series(1782 pd.date_range(1783 '1/1/2000', periods=6, freq='m', tz='America/Los_Angeles'))1784 })1785 DEFERRED_DF = frame_base.DeferredFrame.wrap(1786 expressions.PlaceholderExpression(DF.iloc[:0]))1787 def _run_test(self, fn):1788 expected = fn(self.DF)1789 actual = fn(self.DEFERRED_DF)1790 if isinstance(expected, pd.Index):1791 pd.testing.assert_index_equal(expected, actual)1792 elif isinstance(expected, pd.Series):1793 pd.testing.assert_series_equal(expected, actual)1794 elif isinstance(expected, pd.DataFrame):1795 pd.testing.assert_frame_equal(expected, actual)1796 else:1797 self.assertEqual(expected, actual)1798 @parameterized.expand(DF.columns)1799 def test_series_name(self, col_name):1800 self._run_test(lambda df: df[col_name].name)1801 @parameterized.expand(DF.columns)1802 def test_series_dtype(self, col_name):1803 self._run_test(lambda df: df[col_name].dtype)1804 self._run_test(lambda df: df[col_name].dtypes)1805 def test_dataframe_columns(self):1806 self._run_test(lambda df: list(df.columns))1807 def test_dataframe_dtypes(self):1808 self._run_test(lambda df: list(df.dtypes))1809 def test_categories(self):1810 self._run_test(lambda df: df.cat_col.cat.categories)1811 def test_categorical_ordered(self):1812 self._run_test(lambda df: df.cat_col.cat.ordered)1813 def test_groupby_ndim(self):1814 self._run_test(lambda df: df.groupby('int_col').ndim)1815 def test_groupby_project_ndim(self):1816 self._run_test(lambda df: df.groupby('int_col').flt_col.ndim)1817 self._run_test(1818 lambda df: df.groupby('int_col')[['flt_col', 'str_col']].ndim)1819 def test_get_column_default_None(self):1820 # .get just returns default_value=None at construction time if the column1821 # doesn't exist1822 self._run_test(lambda df: df.get('FOO'))1823 def test_datetime_tz(self):1824 self._run_test(lambda df: df.datetime_col.dt.tz)1825class DocstringTest(unittest.TestCase):1826 @parameterized.expand([1827 (frames.DeferredDataFrame, pd.DataFrame),1828 (frames.DeferredSeries, pd.Series),1829 #(frames._DeferredIndex, pd.Index),1830 (frames._DeferredStringMethods, pd.core.strings.StringMethods),1831 (1832 frames._DeferredCategoricalMethods,1833 pd.core.arrays.categorical.CategoricalAccessor),1834 (frames.DeferredGroupBy, pd.core.groupby.generic.DataFrameGroupBy),1835 (frames._DeferredGroupByCols, pd.core.groupby.generic.DataFrameGroupBy),1836 (1837 frames._DeferredDatetimeMethods,1838 pd.core.indexes.accessors.DatetimeProperties),...
pegparser_test.py
Source:pegparser_test.py
...8import sys9import unittest10from pegparser import *11class PegParserTestCase(unittest.TestCase):12 def _run_test(self, grammar, text, expected,13 strings_are_tokens=False, whitespace_rule=None):14 """Utility for running a parser test and comparing results.15 Program exits (sys.exit) if expected does not match actual.16 Args:17 grammar -- the root rule to be used by the parser.18 text -- the text to parse.19 expected -- the expected abstract syntax tree. None means20 failure is expected.21 strings_are_tokens -- whether strings are treated as tokens.22 whitespace_rule -- the rule used for matching whitespace.23 Default is None, which means that no whitespace is tolerated.24 """25 parser = PegParser(grammar, whitespace_rule,26 strings_are_tokens=strings_are_tokens)27 actual = None28 error = None29 try:30 actual = parser.parse(text)31 except SyntaxError, e:32 error = e33 pass34 if actual != expected:35 msg = '''36CONTENT:37%s38EXPECTED:39%s40ACTUAL:41%s42ERROR: %s''' % (text, pprint.pformat(expected), pprint.pformat(actual), error)43 self.fail(msg)44 def test_sequence(self):45 sequence = SEQUENCE('A', 'BB', 'C')46 self._run_test(grammar=sequence, text='ABBC', expected=['A', 'BB', 'C'])47 self._run_test(grammar=sequence, text='BBAC', expected=None)48 # Syntax Sugar49 sequence = ['A', 'BB', 'C']50 self._run_test(grammar=sequence, text='ABBC', expected=['A', 'BB', 'C'])51 self._run_test(grammar=sequence, text='BBAC', expected=None)52 def test_regex(self):53 regex = re.compile(r'[A-Za-z]*')54 self._run_test(grammar=regex, text='AaBb', expected='AaBb')55 self._run_test(grammar=regex, text='0AaBb', expected=None)56 self._run_test(grammar=regex, text='Aa0Bb', expected=None)57 def test_function(self):58 def Func():59 return 'ABC'60 self._run_test(grammar=Func, text='ABC', expected=('Func', 'ABC'))61 self._run_test(grammar=Func, text='XYZ', expected=None)62 def test_function_label(self):63 def func():64 return 'ABC'65 def _func():66 return 'ABC'67 self._run_test(grammar=func, text='ABC', expected=('func', 'ABC'))68 self._run_test(grammar=_func, text='ABC', expected='ABC')69 def test_label(self):70 sequence = [TOKEN('def'), LABEL('funcName', re.compile(r'[a-z0-9]*')),71 TOKEN('():')]72 self._run_test(grammar=sequence, text='def f1():',73 whitespace_rule=' ', expected=[('funcName', 'f1')])74 self._run_test(grammar=sequence, text='def f2():',75 whitespace_rule=' ', expected=[('funcName', 'f2')])76 def test_or(self):77 grammer = OR('A', 'B')78 self._run_test(grammar=grammer, text='A', expected='A')79 self._run_test(grammar=grammer, text='B', expected='B')80 self._run_test(grammar=grammer, text='C', expected=None)81 def test_maybe(self):82 seq = ['A', MAYBE('B'), 'C']83 self._run_test(grammar=seq, text='ABC', expected=['A', 'B', 'C'])84 self._run_test(grammar=seq, text='ADC', expected=None)85 self._run_test(grammar=seq, text='AC', expected=['A', 'C'])86 self._run_test(grammar=seq, text='AB', expected=None)87 def test_many(self):88 seq = ['A', MANY('B'), 'C']89 self._run_test(grammar=seq, text='ABC', expected=['A', 'B', 'C'])90 self._run_test(grammar=seq, text='ABBBBC',91 expected=['A', 'B', 'B', 'B', 'B', 'C'])92 self._run_test(grammar=seq, text='AC', expected=None)93 def test_many_with_separator(self):94 letter = OR('A', 'B', 'C')95 def _gram():96 return [letter, MAYBE([TOKEN(','), _gram])]97 self._run_test(grammar=_gram, text='A,B,C,B',98 expected=['A', 'B', 'C', 'B'])99 self._run_test(grammar=_gram, text='A B C', expected=None)100 shortergrammar = MANY(letter, TOKEN(','))101 self._run_test(grammar=shortergrammar, text='A,B,C,B',102 expected=['A', 'B', 'C', 'B'])103 self._run_test(grammar=shortergrammar, text='A B C', expected=None)104 def test_raise(self):105 self._run_test(grammar=['A', 'B'], text='AB',106 expected=['A', 'B'])107 try:108 self._run_test(grammar=['A', 'B', RAISE('test')], text='AB',109 expected=None)110 print 'Expected RuntimeError'111 sys.exit(-1)112 except RuntimeError, e:113 return114 def test_whitespace(self):115 gram = MANY('A')116 self._run_test(grammar=gram, text='A A A', expected=None)117 self._run_test(grammar=gram, whitespace_rule=' ', text='A A A',118 expected=['A', 'A', 'A'])119 def test_math_expression_syntax(self):120 operator = LABEL('op', OR('+', '-', '/', '*'))121 literal = LABEL('num', re.compile(r'[0-9]+'))122 def _exp():123 return MANY(OR(literal, [TOKEN('('), _exp, TOKEN(')')]),124 separator=operator)125 self._run_test(grammar=_exp,126 text='(1-2)+3*((4*5)*6)+(7+8/9)-10',127 expected=[[('num', '1'), ('op', '-'), ('num', '2')],128 ('op', '+'),129 ('num', '3'),130 ('op', '*'),131 [[('num', '4'), ('op', '*'), ('num', '5')],132 ('op', '*'), ('num', '6')],133 ('op', '+'),134 [('num', '7'), ('op', '+'), ('num', '8'),135 ('op', '/'), ('num', '9')],136 ('op', '-'),137 ('num', '10')])138 def test_mini_language(self):139 def name():140 return re.compile(r'[a-z]+')141 def var_decl():142 return ['var', name, ';']143 def func_invoke():144 return [name, '(', ')', ';']145 def func_body():146 return MANY(OR(var_decl, func_invoke))147 def func_decl():148 return ['function', name, '(', ')', '{', func_body, '}']149 def args():150 return MANY(name, ',')151 def program():152 return MANY(OR(var_decl, func_decl))153 self._run_test(grammar=program,154 whitespace_rule=OR('\n', ' '),155 strings_are_tokens=True,156 text='var x;\nfunction f(){\n var y;\n g();\n}\n',157 expected=('program',[158 ('var_decl', [('name', 'x')]),159 ('func_decl', [('name', 'f'), ('func_body', [160 ('var_decl', [('name', 'y')]),161 ('func_invoke', [('name', 'g')])])])]))162if __name__ == "__main__":163 logging.config.fileConfig("logging.conf")164 if __name__ == '__main__':...
test_problems.py
Source:test_problems.py
...22from ..problems import *23from ..operators import RandomGenerator24class TestProblemsSimple(unittest.TestCase):25 def test_DTLZ1(self):26 self._run_test(DTLZ1(2))27 28 def test_DTLZ2(self):29 self._run_test(DTLZ2(2))30 31 def test_DTLZ3(self):32 self._run_test(DTLZ3(2))33 34 def test_DTLZ4(self):35 self._run_test(DTLZ4(2))36 37 def test_DTLZ7(self):38 self._run_test(DTLZ7(2))39 def test_WFG1(self):40 self._run_test(WFG1(2))41 def test_WFG2(self):42 self._run_test(WFG2(2))43 44 def test_WFG3(self):45 self._run_test(WFG3(2))46 47 def test_WFG4(self):48 self._run_test(WFG4(2))49 50 def test_WFG5(self):51 self._run_test(WFG5(2))52 53 def test_WFG6(self):54 self._run_test(WFG6(2))55 56 def test_WFG7(self):57 self._run_test(WFG7(2))58 59 def test_WFG8(self):60 self._run_test(WFG8(2))61 62 def test_WFG9(self):63 self._run_test(WFG9(2))64 def test_UF1(self):65 self._run_test(UF1())66 67 def test_UF2(self):68 self._run_test(UF2())69 70 def test_UF3(self):71 self._run_test(UF3())72 73 def test_UF4(self):74 self._run_test(UF4())75 76 def test_UF5(self):77 self._run_test(UF5())78 79 def test_UF6(self):80 self._run_test(UF6())81 82 def test_UF7(self):83 self._run_test(UF7())84 85 def test_UF8(self):86 self._run_test(UF8())87 88 def test_UF9(self):89 self._run_test(UF9())90 91 def test_UF10(self):92 self._run_test(UF10())93 94 def test_UF11(self):95 self._run_test(UF11())96 97 def test_UF12(self):98 self._run_test(UF12())99 100 def test_UF13(self):101 self._run_test(UF13())102 103 def test_CF1(self):104 self._run_test(CF1())105 106 def test_CF2(self):107 self._run_test(CF2())108 109 def test_CF3(self):110 self._run_test(CF3())111 112 def test_CF4(self):113 self._run_test(CF4())114 115 def test_CF5(self):116 self._run_test(CF5())117 118 def test_CF6(self):119 self._run_test(CF6())120 121 def test_CF7(self):122 self._run_test(CF7())123 124 def test_CF8(self):125 self._run_test(CF8())126 127 def test_CF9(self):128 self._run_test(CF9())129 130 def test_CF10(self):131 self._run_test(CF10())132 133 def test_ZDT1(self):134 self._run_test(ZDT1())135 136 def test_ZDT2(self):137 self._run_test(ZDT2())138 139 def test_ZDT3(self):140 self._run_test(ZDT3())141 142 def test_ZDT4(self):143 self._run_test(ZDT4())144 145 def test_ZDT5(self):146 self._run_test(ZDT5())147 148 def test_ZDT6(self):149 self._run_test(ZDT6())150 151 def _run_test(self, problem):152 if hasattr(problem, "random"):153 solution = problem.random()154 else:155 solution = RandomGenerator().generate(problem)156 ...
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