def test_datetime_tests(self):
# TODO(BEAM-10721)
datetimelike_result = doctests.testmod(
pd.core.arrays.datetimelike,
use_beam=False,
skip={
'pandas.core.arrays.datetimelike.AttributesMixin._unbox_scalar': [
'*'
],
'pandas.core.arrays.datetimelike.TimelikeOps.ceil': ['*'],
'pandas.core.arrays.datetimelike.TimelikeOps.floor': ['*'],
'pandas.core.arrays.datetimelike.TimelikeOps.round': ['*'],
})
datetime_result = doctests.testmod(
pd.core.arrays.datetimes,
use_beam=False,
skip={
'pandas.core.arrays.datetimes.DatetimeArray.is_leap_year': ['*'],
'pandas.core.arrays.datetimes.DatetimeArray.is_month_end': ['*'],
'pandas.core.arrays.datetimes.DatetimeArray.is_month_start': ['*'],
'pandas.core.arrays.datetimes.DatetimeArray.is_quarter_end': ['*'],
'pandas.core.arrays.datetimes.DatetimeArray.is_quarter_start': [
'*'
],
'pandas.core.arrays.datetimes.DatetimeArray.is_year_end': ['*'],
'pandas.core.arrays.datetimes.DatetimeArray.is_year_start': ['*'],
'pandas.core.arrays.datetimes.DatetimeArray.to_period': ['*'],
'pandas.core.arrays.datetimes.DatetimeArray.tz_localize': ['*'],
})
self.assertEqual(datetimelike_result.failed, 0)
self.assertEqual(datetime_result.failed, 0)
def test_datetime_tests(self):
# TODO(BEAM-10721)
indexes_accessors_result = doctests.testmod(
pd.core.indexes.accessors,
use_beam=False,
skip={
'pandas.core.indexes.accessors.TimedeltaProperties': [
# Seems like an upstream bug. The property is 'second'
'seconds_series.dt.seconds'
],
# TODO(BEAM-12530): Test data creation fails for these
# s = pd.Series(pd.to_timedelta(np.arange(5), unit="d"))
# pylint: disable=line-too-long
'pandas.core.indexes.accessors.DatetimeProperties.to_pydatetime': [
'*'
],
'pandas.core.indexes.accessors.TimedeltaProperties.components': [
'*'
],
'pandas.core.indexes.accessors.TimedeltaProperties.to_pytimedelta': [
'*'
],
# pylint: enable=line-too-long
})
datetimelike_result = doctests.testmod(
pd.core.arrays.datetimelike, use_beam=False)
datetime_result = doctests.testmod(
pd.core.arrays.datetimes,
use_beam=False,
wont_implement_ok={
'pandas.core.arrays.datetimes.DatetimeArray.to_period': ['*'],
# All tz_localize tests use unsupported values for ambiguous=
# Verified seperately in
# frames_test.py::DeferredFrameTest::test_dt_tz_localize_*
'pandas.core.arrays.datetimes.DatetimeArray.tz_localize': ['*'],
},
not_implemented_ok={
# Verifies index version of this method
'pandas.core.arrays.datetimes.DatetimeArray.to_period': [
'df.index.to_period("M")'
],
})
self.assertEqual(indexes_accessors_result.failed, 0)
self.assertEqual(datetimelike_result.failed, 0)
self.assertEqual(datetime_result.failed, 0)
def test_string_tests(self):
result = doctests.testmod(
pd.core.strings,
use_beam=False,
wont_implement_ok={
# These methods can accept deferred series objects, but not lists
'pandas.core.strings.StringMethods.cat': [
"s.str.cat(['A', 'B', 'C', 'D'], sep=',')",
"s.str.cat(['A', 'B', 'C', 'D'], sep=',', na_rep='-')",
"s.str.cat(['A', 'B', 'C', 'D'], na_rep='-')"
],
'pandas.core.strings.StringMethods.repeat': [
's.str.repeat(repeats=[1, 2, 3])'
],
'pandas.core.strings.str_repeat': [
's.str.repeat(repeats=[1, 2, 3])'
],
},
skip={
# Bad test strings
'pandas.core.strings.str_replace': [
"pd.Series(['foo', 'fuz', np.nan]).str.replace('f', repr)"
],
'pandas.core.strings.StringMethods.replace': [
"pd.Series(['foo', 'fuz', np.nan]).str.replace('f', repr)"
],
})
self.assertEqual(result.failed, 0)
def test_string_tests(self):
result = doctests.testmod(
pd.core.strings,
use_beam=False,
skip={
'pandas.core.strings.StringMethods.cat': ['*'],
'pandas.core.strings.StringMethods.repeat': ['*'],
'pandas.core.strings.str_repeat': ['*'],
# The rest of the skipped tests represent bad test strings,
# fixed upstream in
# https://github.com/pandas-dev/pandas/commit/d095ac899da953d759992824592a72a1e6ff5e09
'pandas.core.strings.StringMethods':
["s.str.split('_')", "s.str.replace('_', '')"],
'pandas.core.strings.str_split': ["s.str.split(expand=True)"],
'pandas.core.strings.str_replace':
["pd.Series(['foo', 'fuz', np.nan]).str.replace('f', repr)"],
'pandas.core.strings.StringMethods.replace':
["pd.Series(['foo', 'fuz', np.nan]).str.replace('f', repr)"],
'pandas.core.strings.StringMethods.partition':
['idx.str.partition()'],
'pandas.core.strings.StringMethods.rpartition':
['idx.str.partition()'],
# rsplit/split are particularly troublesome because the first test,
# defining a test series, is bad and must be skipped. But skipping
# it breaks every other test. To run the rest we would need to
# execute the first test but ignore the output.
'pandas.core.strings.StringMethods.rsplit': ["*"],
'pandas.core.strings.StringMethods.split': ["*"],
})
self.assertEqual(result.failed, 0)
def test_dataframe_tests(self):
result = doctests.testmod(
pd.core.frame,
use_beam=False,
skip={
'pandas.core.frame.DataFrame.T': ['*'],
'pandas.core.frame.DataFrame.agg': ['*'],
'pandas.core.frame.DataFrame.aggregate': ['*'],
'pandas.core.frame.DataFrame.append': ['*'],
'pandas.core.frame.DataFrame.apply': ['*'],
'pandas.core.frame.DataFrame.applymap': ['df ** 2'],
'pandas.core.frame.DataFrame.assign': ['*'],
'pandas.core.frame.DataFrame.axes': ['*'],
'pandas.core.frame.DataFrame.combine': ['*'],
'pandas.core.frame.DataFrame.combine_first': ['*'],
'pandas.core.frame.DataFrame.corr': ['*'],
'pandas.core.frame.DataFrame.count': ['*'],
'pandas.core.frame.DataFrame.cov': ['*'],
'pandas.core.frame.DataFrame.dot': ['*'],
'pandas.core.frame.DataFrame.drop': ['*'],
'pandas.core.frame.DataFrame.eval': ['*'],
'pandas.core.frame.DataFrame.explode': ['*'],
'pandas.core.frame.DataFrame.fillna': ['*'],
'pandas.core.frame.DataFrame.info': ['*'],
'pandas.core.frame.DataFrame.isin': ['*'],
'pandas.core.frame.DataFrame.iterrows':
["print(df['int'].dtype)"],
'pandas.core.frame.DataFrame.join': ['*'],
'pandas.core.frame.DataFrame.melt': ['*'],
'pandas.core.frame.DataFrame.memory_usage': ['*'],
'pandas.core.frame.DataFrame.merge': ['*'],
# Not equal to df.agg('mode', axis='columns', numeric_only=True)
'pandas.core.frame.DataFrame.mode':
["df.mode(axis='columns', numeric_only=True)"],
'pandas.core.frame.DataFrame.nlargest': ['*'],
'pandas.core.frame.DataFrame.nsmallest': ['*'],
'pandas.core.frame.DataFrame.nunique': ['*'],
'pandas.core.frame.DataFrame.pivot': ['*'],
'pandas.core.frame.DataFrame.pivot_table': ['*'],
'pandas.core.frame.DataFrame.query': ['*'],
'pandas.core.frame.DataFrame.reindex': ['*'],
'pandas.core.frame.DataFrame.reindex_axis': ['*'],
'pandas.core.frame.DataFrame.rename': ['*'],
# Raises right exception, but testing framework has matching issues.
'pandas.core.frame.DataFrame.replace': [
"df.replace({'a string': 'new value', True: False}) # raises"
],
# Uses unseeded np.random.
'pandas.core.frame.DataFrame.round': ['*'],
'pandas.core.frame.DataFrame.set_index': ['*'],
'pandas.core.frame.DataFrame.transpose':
['df1_transposed.dtypes', 'df2_transposed.dtypes'],
'pandas.core.frame.DataFrame.to_sparse': ['type(df)'],
# Uses df.index
'pandas.core.frame.DataFrame.to_records': ['*'],
})
self.assertEqual(result.failed, 0)
def test_string_tests(self):
PD_VERSION = tuple(int(v) for v in pd.__version__.split('.'))
if PD_VERSION < (1, 2, 0):
module = pd.core.strings
else:
# Definitions were moved to accessor in pandas 1.2.0
module = pd.core.strings.accessor
module_name = module.__name__
result = doctests.testmod(
module,
use_beam=False,
wont_implement_ok={
# These methods can accept deferred series objects, but not lists
f'{module_name}.StringMethods.cat': [
"s.str.cat(['A', 'B', 'C', 'D'], sep=',')",
"s.str.cat(['A', 'B', 'C', 'D'], sep=',', na_rep='-')",
"s.str.cat(['A', 'B', 'C', 'D'], na_rep='-')"
],
f'{module_name}.StringMethods.repeat': [
's.str.repeat(repeats=[1, 2, 3])'
],
f'{module_name}.str_repeat': ['s.str.repeat(repeats=[1, 2, 3])'],
f'{module_name}.StringMethods.get_dummies': ['*'],
f'{module_name}.str_get_dummies': ['*'],
},
skip={
# count() on Series with a NaN produces mismatched type if we
# have a NaN-only partition.
f'{module_name}.StringMethods.count': ["s.str.count('a')"],
f'{module_name}.str_count': ["s.str.count('a')"],
# Produce None instead of NaN, see
# frames_test.py::DeferredFrameTest::test_str_split
f'{module_name}.StringMethods.rsplit': [
's.str.split(expand=True)',
's.str.rsplit("/", n=1, expand=True)',
],
f'{module_name}.StringMethods.split': [
's.str.split(expand=True)',
's.str.rsplit("/", n=1, expand=True)',
],
# Bad test strings in pandas 1.1.x
f'{module_name}.str_replace': [
"pd.Series(['foo', 'fuz', np.nan]).str.replace('f', repr)"
],
f'{module_name}.StringMethods.replace': [
"pd.Series(['foo', 'fuz', np.nan]).str.replace('f', repr)"
],
# output has incorrect formatting in 1.2.x
f'{module_name}.StringMethods.extractall': ['*']
})
self.assertEqual(result.failed, 0)
def test_series_tests(self):
result = doctests.testmod(
pd.core.series,
use_beam=False,
skip={
'pandas.core.series.Series.append': ['*'],
'pandas.core.series.Series.argmax': ['*'],
'pandas.core.series.Series.argmin': ['*'],
'pandas.core.series.Series.autocorr': ['*'],
'pandas.core.series.Series.between': ['*'],
'pandas.core.series.Series.combine': ['*'],
'pandas.core.series.Series.combine_first': ['*'],
'pandas.core.series.Series.corr': ['*'],
'pandas.core.series.Series.count': ['*'],
'pandas.core.series.Series.cov': ['*'],
'pandas.core.series.Series.dot': ['*'],
'pandas.core.series.Series.drop': ['*'],
'pandas.core.series.Series.drop_duplicates': ['*'],
'pandas.core.series.Series.dropna': ['*'],
'pandas.core.series.Series.duplicated': ['*'],
'pandas.core.series.Series.explode': ['*'],
'pandas.core.series.Series.fillna': ['*'],
'pandas.core.series.Series.idxmax': ['*'],
'pandas.core.series.Series.idxmin': ['*'],
'pandas.core.series.Series.isin': ['*'],
'pandas.core.series.Series.items': ['*'],
'pandas.core.series.Series.iteritems': ['*'],
'pandas.core.series.Series.memory_usage': ['*'],
'pandas.core.series.Series.nlargest': ['*'],
'pandas.core.series.Series.nonzero': ['*'],
'pandas.core.series.Series.nsmallest': ['*'],
'pandas.core.series.Series.quantile': ['*'],
'pandas.core.series.Series.reindex': ['*'],
'pandas.core.series.Series.rename': ['*'],
'pandas.core.series.Series.repeat': ['*'],
'pandas.core.series.Series.replace': ['*'],
'pandas.core.series.Series.reset_index': ['*'],
'pandas.core.series.Series.round': ['*'],
'pandas.core.series.Series.searchsorted': ['*'],
'pandas.core.series.Series.shift': ['*'],
'pandas.core.series.Series.sort_index': ['*'],
'pandas.core.series.Series.sort_values': ['*'],
'pandas.core.series.Series.take': ['*'],
'pandas.core.series.Series.to_csv': ['*'],
'pandas.core.series.Series.to_dict': ['*'],
'pandas.core.series.Series.to_frame': ['*'],
'pandas.core.series.Series.unique': ['*'],
'pandas.core.series.Series.update': ['*'],
'pandas.core.series.Series.values': ['*'],
'pandas.core.series.Series.view': ['*'],
})
self.assertEqual(result.failed, 0)
def test_string_tests(self):
if PD_VERSION < (1, 2):
module = pd.core.strings
else:
# Definitions were moved to accessor in pandas 1.2.0
module = pd.core.strings.accessor
module_name = module.__name__
result = doctests.testmod(
module,
use_beam=False,
wont_implement_ok={
# These methods can accept deferred series objects, but not lists
f'{module_name}.StringMethods.cat': [
"s.str.cat(['A', 'B', 'C', 'D'], sep=',')",
"s.str.cat(['A', 'B', 'C', 'D'], sep=',', na_rep='-')",
"s.str.cat(['A', 'B', 'C', 'D'], na_rep='-')"
],
f'{module_name}.StringMethods.repeat': [
's.str.repeat(repeats=[1, 2, 3])'
],
f'{module_name}.str_repeat': ['s.str.repeat(repeats=[1, 2, 3])'],
# get_dummies pandas examples are not casted to CategoricalDtype
# Must be CategoricalDtype to work in Beam
f'{module_name}.StringMethods.get_dummies': ['*'],
f'{module_name}.str_get_dummies': ['*'],
f'{module_name}.StringMethods': ['s.str.split("_")'],
f'{module_name}.StringMethods.rsplit': ['*'],
f'{module_name}.StringMethods.split': ['*'],
},
skip={
# count() on Series with a NaN produces mismatched type if we
# have a NaN-only partition.
f'{module_name}.StringMethods.count': ["s.str.count('a')"],
f'{module_name}.str_count': ["s.str.count('a')"],
# Bad test strings in pandas 1.1.x
f'{module_name}.str_replace': [
"pd.Series(['foo', 'fuz', np.nan]).str.replace('f', repr)"
],
f'{module_name}.StringMethods.replace': [
"pd.Series(['foo', 'fuz', np.nan]).str.replace('f', repr)"
],
# output has incorrect formatting in 1.2.x
f'{module_name}.StringMethods.extractall': ['*']
})
self.assertEqual(result.failed, 0)
def test_indexing_tests(self):
result = doctests.testmod(pd.core.indexing,
use_beam=False,
skip={
'pandas.core.indexing._AtIndexer': ['*'],
'pandas.core.indexing._IndexSlice':
['*'],
'pandas.core.indexing._LocIndexer':
['*'],
'pandas.core.indexing._iAtIndexer':
['*'],
'pandas.core.indexing._iLocIndexer':
['*'],
})
self.assertEqual(result.failed, 0)
def test_series_tests(self):
result = doctests.testmod(
pd.core.series,
use_beam=False,
report=True,
wont_implement_ok={
'pandas.core.series.Series.__array__': ['*'],
'pandas.core.series.Series.array': ['*'],
'pandas.core.series.Series.cummax': ['*'],
'pandas.core.series.Series.cummin': ['*'],
'pandas.core.series.Series.cumsum': ['*'],
'pandas.core.series.Series.cumprod': ['*'],
'pandas.core.series.Series.diff': ['*'],
'pandas.core.series.Series.dot': [
's.dot(arr)', # non-deferred result
],
'pandas.core.series.Series.fillna': [
"df.fillna(method='ffill')",
'df.fillna(value=values, limit=1)',
],
'pandas.core.series.Series.items': ['*'],
'pandas.core.series.Series.iteritems': ['*'],
# default keep is 'first'
'pandas.core.series.Series.nlargest': [
"s.nlargest()",
"s.nlargest(3)",
"s.nlargest(3, keep='last')",
],
'pandas.core.series.Series.memory_usage': ['*'],
'pandas.core.series.Series.nsmallest': [
"s.nsmallest()",
"s.nsmallest(3)",
"s.nsmallest(3, keep='last')",
],
'pandas.core.series.Series.pop': ['*'],
'pandas.core.series.Series.searchsorted': ['*'],
'pandas.core.series.Series.shift': ['*'],
'pandas.core.series.Series.take': ['*'],
'pandas.core.series.Series.to_dict': ['*'],
'pandas.core.series.Series.unique': ['*'],
'pandas.core.series.Series.unstack': ['*'],
'pandas.core.series.Series.values': ['*'],
'pandas.core.series.Series.view': ['*'],
'pandas.core.series.Series.append': [
's1.append(s2, ignore_index=True)',
],
},
not_implemented_ok={
'pandas.core.series.Series.transform': ['*'],
'pandas.core.series.Series.groupby': [
'ser.groupby(["a", "b", "a", "b"]).mean()',
'ser.groupby(["a", "b", "a", np.nan]).mean()',
'ser.groupby(["a", "b", "a", np.nan], dropna=False).mean()',
# Grouping by a series is not supported
'ser.groupby(ser > 100).mean()',
],
'pandas.core.series.Series.reindex': ['*'],
},
skip={
# error formatting
'pandas.core.series.Series.append': [
's1.append(s2, verify_integrity=True)',
],
# Throws NotImplementedError when modifying df
'pandas.core.series.Series.transform': ['df'],
'pandas.core.series.Series.argmax': ['*'],
'pandas.core.series.Series.argmin': ['*'],
'pandas.core.series.Series.autocorr': ['*'],
'pandas.core.series.Series.combine': ['*'],
'pandas.core.series.Series.combine_first': ['*'],
'pandas.core.series.Series.compare': ['*'],
'pandas.core.series.Series.cov': [
# Differs in LSB on jenkins.
"s1.cov(s2)",
],
'pandas.core.series.Series.drop_duplicates': ['*'],
'pandas.core.series.Series.duplicated': ['*'],
'pandas.core.series.Series.explode': ['*'],
'pandas.core.series.Series.idxmax': ['*'],
'pandas.core.series.Series.idxmin': ['*'],
'pandas.core.series.Series.name': ['*'],
'pandas.core.series.Series.nonzero': ['*'],
'pandas.core.series.Series.quantile': ['*'],
'pandas.core.series.Series.pop':
['ser'], # testing side effect
'pandas.core.series.Series.repeat': ['*'],
'pandas.core.series.Series.replace': ['*'],
'pandas.core.series.Series.reset_index': ['*'],
'pandas.core.series.Series.searchsorted': [
# This doctest seems to be incorrectly parsed.
"x = pd.Categorical(['apple', 'bread', 'bread',"
],
'pandas.core.series.Series.set_axis': ['*'],
'pandas.core.series.Series.sort_index': ['*'],
'pandas.core.series.Series.sort_values': ['*'],
'pandas.core.series.Series.to_csv': ['*'],
'pandas.core.series.Series.to_markdown': ['*'],
'pandas.core.series.Series.update': ['*'],
'pandas.core.series.Series.view': [
# Inspection after modification.
's'
],
})
self.assertEqual(result.failed, 0)
def test_groupby_tests(self):
result = doctests.testmod(
pd.core.groupby.groupby,
use_beam=False,
wont_implement_ok={
'pandas.core.groupby.groupby.GroupBy.head': ['*'],
'pandas.core.groupby.groupby.GroupBy.tail': ['*'],
'pandas.core.groupby.groupby.GroupBy.nth': ['*'],
'pandas.core.groupby.groupby.GroupBy.cumcount': ['*'],
},
not_implemented_ok={
'pandas.core.groupby.groupby.GroupBy.describe': ['*'],
'pandas.core.groupby.groupby.GroupBy.ngroup': ['*'],
'pandas.core.groupby.groupby.GroupBy.resample': ['*'],
'pandas.core.groupby.groupby.GroupBy.sample': ['*'],
'pandas.core.groupby.groupby.GroupBy.quantile': ['*'],
'pandas.core.groupby.groupby.BaseGroupBy.pipe': ['*'],
# pipe tests are in a different location in pandas 1.1.x
'pandas.core.groupby.groupby._GroupBy.pipe': ['*'],
'pandas.core.groupby.groupby.GroupBy.nth': [
"df.groupby('A', as_index=False).nth(1)",
],
},
skip={
# Uses iloc to mutate a DataFrame
'pandas.core.groupby.groupby.GroupBy.resample': [
'df.iloc[2, 0] = 5',
'df',
],
# TODO: Raise wont implement for list passed as a grouping column
# Currently raises unhashable type: list
'pandas.core.groupby.groupby.GroupBy.ngroup':
['df.groupby(["A", [1,1,2,3,2,1]]).ngroup()'],
})
self.assertEqual(result.failed, 0)
result = doctests.testmod(
pd.core.groupby.generic,
use_beam=False,
wont_implement_ok={
# Returns an array by default, not a Series. WontImplement
# (non-deferred)
'pandas.core.groupby.generic.SeriesGroupBy.unique': ['*'],
# TODO: Is take actually deprecated?
'pandas.core.groupby.generic.DataFrameGroupBy.take': ['*'],
'pandas.core.groupby.generic.SeriesGroupBy.take': ['*'],
'pandas.core.groupby.generic.SeriesGroupBy.nsmallest': [
"s.nsmallest(3, keep='last')",
"s.nsmallest(3)",
"s.nsmallest()",
],
'pandas.core.groupby.generic.SeriesGroupBy.nlargest': [
"s.nlargest(3, keep='last')",
"s.nlargest(3)",
"s.nlargest()",
],
'pandas.core.groupby.generic.DataFrameGroupBy.diff': ['*'],
'pandas.core.groupby.generic.SeriesGroupBy.diff': ['*'],
'pandas.core.groupby.generic.DataFrameGroupBy.hist': ['*'],
'pandas.core.groupby.generic.DataFrameGroupBy.fillna': [
"df.fillna(method='ffill')",
'df.fillna(value=values, limit=1)',
],
'pandas.core.groupby.generic.SeriesGroupBy.fillna': [
"df.fillna(method='ffill')",
'df.fillna(value=values, limit=1)',
],
},
not_implemented_ok={
'pandas.core.groupby.generic.DataFrameGroupBy.transform':
['*'],
'pandas.core.groupby.generic.DataFrameGroupBy.idxmax': ['*'],
'pandas.core.groupby.generic.DataFrameGroupBy.idxmin': ['*'],
'pandas.core.groupby.generic.DataFrameGroupBy.filter': ['*'],
'pandas.core.groupby.generic.DataFrameGroupBy.nunique': ['*'],
'pandas.core.groupby.generic.SeriesGroupBy.transform': ['*'],
'pandas.core.groupby.generic.SeriesGroupBy.idxmax': ['*'],
'pandas.core.groupby.generic.SeriesGroupBy.idxmin': ['*'],
'pandas.core.groupby.generic.SeriesGroupBy.filter': ['*'],
'pandas.core.groupby.generic.SeriesGroupBy.describe': ['*'],
},
skip={
'pandas.core.groupby.generic.SeriesGroupBy.cov': [
# Floating point comparison fails
's1.cov(s2)',
],
'pandas.core.groupby.generic.DataFrameGroupBy.cov': [
# Mutates input DataFrame with loc
# TODO: Replicate in frames_test.py
"df.loc[df.index[:5], 'a'] = np.nan",
"df.loc[df.index[5:10], 'b'] = np.nan",
"df.cov(min_periods=12)",
],
# These examples rely on grouping by a list
'pandas.core.groupby.generic.SeriesGroupBy.aggregate': ['*'],
'pandas.core.groupby.generic.DataFrameGroupBy.aggregate':
['*'],
})
self.assertEqual(result.failed, 0)
def test_dataframe_tests(self):
result = doctests.testmod(
pd.core.frame,
use_beam=False,
report=True,
wont_implement_ok={
'pandas.core.frame.DataFrame.T': ['*'],
'pandas.core.frame.DataFrame.cummax': ['*'],
'pandas.core.frame.DataFrame.cummin': ['*'],
'pandas.core.frame.DataFrame.cumsum': ['*'],
'pandas.core.frame.DataFrame.cumprod': ['*'],
'pandas.core.frame.DataFrame.diff': ['*'],
'pandas.core.frame.DataFrame.fillna': [
"df.fillna(method='ffill')",
'df.fillna(value=values, limit=1)',
],
'pandas.core.frame.DataFrame.items': ['*'],
'pandas.core.frame.DataFrame.itertuples': ['*'],
'pandas.core.frame.DataFrame.iterrows': ['*'],
'pandas.core.frame.DataFrame.iteritems': ['*'],
# default keep is 'first'
'pandas.core.frame.DataFrame.nlargest': [
"df.nlargest(3, 'population')",
"df.nlargest(3, ['population', 'GDP'])",
"df.nlargest(3, 'population', keep='last')"
],
'pandas.core.frame.DataFrame.nsmallest': [
"df.nsmallest(3, 'population')",
"df.nsmallest(3, ['population', 'GDP'])",
"df.nsmallest(3, 'population', keep='last')",
],
'pandas.core.frame.DataFrame.replace': [
"s.replace([1, 2], method='bfill')",
# Relies on method='pad'
"s.replace('a', None)",
],
'pandas.core.frame.DataFrame.to_records': ['*'],
'pandas.core.frame.DataFrame.to_dict': ['*'],
'pandas.core.frame.DataFrame.to_numpy': ['*'],
'pandas.core.frame.DataFrame.to_string': ['*'],
'pandas.core.frame.DataFrame.transpose': ['*'],
'pandas.core.frame.DataFrame.shape': ['*'],
'pandas.core.frame.DataFrame.shift': [
'df.shift(periods=3, freq="D")',
'df.shift(periods=3, freq="infer")'
],
'pandas.core.frame.DataFrame.unstack': ['*'],
'pandas.core.frame.DataFrame.memory_usage': ['*'],
'pandas.core.frame.DataFrame.info': ['*'],
# Not equal to df.agg('mode', axis='columns', numeric_only=True)
# because there can be multiple columns if a row has more than one
# mode
'pandas.core.frame.DataFrame.mode':
["df.mode(axis='columns', numeric_only=True)"],
'pandas.core.frame.DataFrame.append': [
'df.append(df2, ignore_index=True)',
"for i in range(5):\n" +
" df = df.append({'A': i}, ignore_index=True)",
],
},
not_implemented_ok={
'pandas.core.frame.DataFrame.transform': ['*'],
'pandas.core.frame.DataFrame.isin': ['*'],
'pandas.core.frame.DataFrame.melt': ['*'],
'pandas.core.frame.DataFrame.reindex': ['*'],
'pandas.core.frame.DataFrame.reindex_axis': ['*'],
'pandas.core.frame.DataFrame.round': [
'df.round(decimals)',
],
# We should be able to support pivot and pivot_table for categorical
# columns
'pandas.core.frame.DataFrame.pivot': ['*'],
# We can implement this as a zipping operator, but it won't have the
# same capability. The doctest includes an example that branches on
# a deferred result.
'pandas.core.frame.DataFrame.combine': ['*'],
# Can be implemented as a zipping operator
'pandas.core.frame.DataFrame.combine_first': ['*'],
# Difficult to parallelize but should be possible?
'pandas.core.frame.DataFrame.dot': [
# reindex not supported
's2 = s.reindex([1, 0, 2, 3])',
'df.dot(s2)',
],
# Trivially elementwise for axis=columns. Relies on global indexing
# for axis=rows.
# Difficult to determine proxy, need to inspect function
'pandas.core.frame.DataFrame.apply': ['*'],
# Cross-join not implemented
'pandas.core.frame.DataFrame.merge':
["df1.merge(df2, how='cross')"],
# TODO(BEAM-11711)
'pandas.core.frame.DataFrame.set_index': [
"df.set_index([s, s**2])",
],
},
skip={
# Throws NotImplementedError when modifying df
'pandas.core.frame.DataFrame.transform': ['df'],
'pandas.core.frame.DataFrame.axes': [
# Returns deferred index.
'df.axes',
],
'pandas.core.frame.DataFrame.compare': ['*'],
'pandas.core.frame.DataFrame.cov': [
# Relies on setting entries ahead of time.
"df.loc[df.index[:5], 'a'] = np.nan",
"df.loc[df.index[5:10], 'b'] = np.nan",
'df.cov(min_periods=12)',
],
'pandas.core.frame.DataFrame.drop_duplicates': ['*'],
'pandas.core.frame.DataFrame.duplicated': ['*'],
'pandas.core.frame.DataFrame.idxmax': ['*'],
'pandas.core.frame.DataFrame.idxmin': ['*'],
'pandas.core.frame.DataFrame.rename': [
# Returns deferred index.
'df.index',
'df.rename(index=str).index',
],
'pandas.core.frame.DataFrame.set_index': [
# TODO(BEAM-11711): This could pass in the index as
# a DeferredIndex, and we should fail it as order-sensitive.
"df.set_index([pd.Index([1, 2, 3, 4]), 'year'])",
],
'pandas.core.frame.DataFrame.set_axis': ['*'],
'pandas.core.frame.DataFrame.sort_index': ['*'],
'pandas.core.frame.DataFrame.to_markdown': ['*'],
'pandas.core.frame.DataFrame.to_parquet': ['*'],
'pandas.core.frame.DataFrame.value_counts': ['*'],
'pandas.core.frame.DataFrame.to_records': [
'df.index = df.index.rename("I")',
'index_dtypes = f"<S{df.index.str.len().max()}"', # 1.x
'index_dtypes = "<S{}".format(df.index.str.len().max())', #0.x
'df.to_records(index_dtypes=index_dtypes)',
],
# These tests use the static method pd.pivot_table, which doesn't
# actually raise NotImplementedError
'pandas.core.frame.DataFrame.pivot_table': ['*'],
# Expected to raise a ValueError, but we raise NotImplementedError
'pandas.core.frame.DataFrame.pivot':
["df.pivot(index='foo', columns='bar', values='baz')"],
'pandas.core.frame.DataFrame.append': [
'df',
# pylint: disable=line-too-long
"pd.concat([pd.DataFrame([i], columns=['A']) for i in range(5)],\n"
" ignore_index=True)"
],
'pandas.core.frame.DataFrame.eval': ['df'],
'pandas.core.frame.DataFrame.melt':
["df.columns = [list('ABC'), list('DEF')]", "df"],
'pandas.core.frame.DataFrame.merge': [
# Order-sensitive index, checked in frames_test.py.
"df1.merge(df2, left_on='lkey', right_on='rkey')",
"df1.merge(df2, left_on='lkey', right_on='rkey',\n"
" suffixes=('_left', '_right'))",
"df1.merge(df2, how='left', on='a')",
],
# Raises right exception, but testing framework has matching issues.
'pandas.core.frame.DataFrame.replace': [
"df.replace({'a string': 'new value', True: False}) # raises"
],
'pandas.core.frame.DataFrame.to_sparse': ['type(df)'],
# Skipped because "seen_wont_implement" is reset before getting to
# these calls, so the NameError they raise is not ignored.
'pandas.core.frame.DataFrame.T':
['df1_transposed.dtypes', 'df2_transposed.dtypes'],
'pandas.core.frame.DataFrame.transpose':
['df1_transposed.dtypes', 'df2_transposed.dtypes'],
# Skipped because the relies on iloc to set a cell to NA. Test is
# replicated in frames_test::DeferredFrameTest::test_applymap.
'pandas.core.frame.DataFrame.applymap': [
'df_copy.iloc[0, 0] = pd.NA',
"df_copy.applymap(lambda x: len(str(x)), na_action='ignore')",
],
# Skipped so we don't need to install natsort
'pandas.core.frame.DataFrame.sort_values': [
'from natsort import index_natsorted', 'df.sort_values(\n'
' by="time",\n'
' key=lambda x: np.argsort(index_natsorted(df["time"]))\n'
')'
],
# Mode that we don't yet support, documentation added in pandas
# 1.2.0 (https://github.com/pandas-dev/pandas/issues/35912)
'pandas.core.frame.DataFrame.aggregate':
["df.agg(x=('A', max), y=('B', 'min'), z=('C', np.mean))"],
})
self.assertEqual(result.failed, 0)
def test_ndframe_tests(self):
# IO methods are tested in io_test.py
skip_writes = {
f'pandas.core.generic.NDFrame.{name}': ['*']
for name in dir(pd.core.generic.NDFrame) if name.startswith('to_')
}
result = doctests.testmod(
pd.core.generic,
use_beam=False,
report=True,
wont_implement_ok={
'pandas.core.generic.NDFrame.first': ['*'],
'pandas.core.generic.NDFrame.head': ['*'],
'pandas.core.generic.NDFrame.last': ['*'],
'pandas.core.generic.NDFrame.shift': ['*'],
'pandas.core.generic.NDFrame.tail': ['*'],
'pandas.core.generic.NDFrame.take': ['*'],
'pandas.core.generic.NDFrame.values': ['*'],
'pandas.core.generic.NDFrame.tz_localize': [
"s.tz_localize('CET', ambiguous='infer')",
# np.array is not a deferred object. This use-case is possible
# with a deferred Series though, which is tested in
# frames_test.py
"s.tz_localize('CET', ambiguous=np.array([True, True, False]))",
],
'pandas.core.generic.NDFrame.truncate': [
# These inputs rely on tail (wont implement, order
# sensitive) for verification
"df.tail()",
"df.loc['2016-01-05':'2016-01-10', :].tail()",
],
'pandas.core.generic.NDFrame.replace': [
"s.replace([1, 2], method='bfill')",
# Relies on method='pad'
"s.replace('a', None)",
],
'pandas.core.generic.NDFrame.fillna': [
"df.fillna(method='ffill')",
'df.fillna(value=values, limit=1)',
],
},
not_implemented_ok={
'pandas.core.generic.NDFrame.add_prefix': ['*'],
'pandas.core.generic.NDFrame.add_suffix': ['*'],
'pandas.core.generic.NDFrame.asof': ['*'],
'pandas.core.generic.NDFrame.at_time': ['*'],
'pandas.core.generic.NDFrame.between_time': ['*'],
'pandas.core.generic.NDFrame.describe': ['*'],
'pandas.core.generic.NDFrame.ewm': ['*'],
'pandas.core.generic.NDFrame.expanding': ['*'],
'pandas.core.generic.NDFrame.flags': ['*'],
'pandas.core.generic.NDFrame.interpolate': ['*'],
'pandas.core.generic.NDFrame.mask': ['*'],
'pandas.core.generic.NDFrame.pct_change': ['*'],
'pandas.core.generic.NDFrame.rank': ['*'],
'pandas.core.generic.NDFrame.reindex': ['*'],
'pandas.core.generic.NDFrame.reindex_like': ['*'],
'pandas.core.generic.NDFrame.replace': ['*'],
'pandas.core.generic.NDFrame.resample': ['*'],
'pandas.core.generic.NDFrame.rolling': ['*'],
'pandas.core.generic.NDFrame.sample': ['*'],
'pandas.core.generic.NDFrame.set_flags': ['*'],
'pandas.core.generic.NDFrame.squeeze': ['*'],
'pandas.core.generic.NDFrame.transform': ['*'],
'pandas.core.generic.NDFrame.truncate': ['*'],
'pandas.core.generic.NDFrame.where': ['*'],
'pandas.core.generic.NDFrame.xs': ['*'],
# argsort unimplemented
'pandas.core.generic.NDFrame.abs': [
'df.loc[(df.c - 43).abs().argsort()]',
],
},
skip={
# Internal test
'pandas.core.generic.NDFrame._set_axis_name': ['*'],
# Fails to construct test series. asfreq is not implemented anyway.
'pandas.core.generic.NDFrame.asfreq': ['*'],
'pandas.core.generic.NDFrame.astype': ['*'],
'pandas.core.generic.NDFrame.convert_dtypes': ['*'],
'pandas.core.generic.NDFrame.copy': ['*'],
'pandas.core.generic.NDFrame.droplevel': ['*'],
'pandas.core.generic.NDFrame.infer_objects': ['*'],
'pandas.core.generic.NDFrame.rank': [
# Modified dataframe
'df'
],
'pandas.core.generic.NDFrame.rename': [
# Seems to be an upstream bug. The actual error has a different
# message:
# TypeError: Index(...) must be called with a collection of
# some kind, 2 was passed
# pandas doctests only verify the type of exception
'df.rename(2)'
],
# Tests rely on setting index
'pandas.core.generic.NDFrame.rename_axis': ['*'],
# Raises right exception, but testing framework has matching issues.
'pandas.core.generic.NDFrame.replace': [
"df.replace({'a string': 'new value', True: False}) # raises"
],
'pandas.core.generic.NDFrame.squeeze': ['*'],
# NameError
'pandas.core.generic.NDFrame.resample': ['df'],
# Skipped so we don't need to install natsort
'pandas.core.generic.NDFrame.sort_values': [
'from natsort import index_natsorted', 'df.sort_values(\n'
' by="time",\n'
' key=lambda x: np.argsort(index_natsorted(df["time"]))\n'
')'
],
**skip_writes
})
self.assertEqual(result.failed, 0)
def test_dataframe_tests(self):
result = doctests.testmod(
pd.core.frame,
use_beam=False,
report=True,
wont_implement_ok={
'pandas.core.frame.DataFrame.T': ['*'],
'pandas.core.frame.DataFrame.cummax': ['*'],
'pandas.core.frame.DataFrame.cummin': ['*'],
'pandas.core.frame.DataFrame.cumsum': ['*'],
'pandas.core.frame.DataFrame.cumprod': ['*'],
'pandas.core.frame.DataFrame.diff': ['*'],
'pandas.core.frame.DataFrame.items': ['*'],
'pandas.core.frame.DataFrame.itertuples': ['*'],
'pandas.core.frame.DataFrame.iterrows': ['*'],
'pandas.core.frame.DataFrame.iteritems': ['*'],
# default keep is 'first'
'pandas.core.frame.DataFrame.nlargest': [
"df.nlargest(3, 'population')",
"df.nlargest(3, ['population', 'GDP'])",
"df.nlargest(3, 'population', keep='last')"
],
'pandas.core.frame.DataFrame.nsmallest': [
"df.nsmallest(3, 'population')",
"df.nsmallest(3, ['population', 'GDP'])",
"df.nsmallest(3, 'population', keep='last')",
],
'pandas.core.frame.DataFrame.nunique': ['*'],
'pandas.core.frame.DataFrame.to_records': ['*'],
'pandas.core.frame.DataFrame.to_dict': ['*'],
'pandas.core.frame.DataFrame.to_numpy': ['*'],
'pandas.core.frame.DataFrame.to_string': ['*'],
'pandas.core.frame.DataFrame.transpose': ['*'],
'pandas.core.frame.DataFrame.shape': ['*'],
'pandas.core.frame.DataFrame.shift': [
'df.shift(periods=3, freq="D")',
'df.shift(periods=3, freq="infer")'
],
'pandas.core.frame.DataFrame.unstack': ['*'],
'pandas.core.frame.DataFrame.memory_usage': ['*'],
'pandas.core.frame.DataFrame.info': ['*'],
# Not equal to df.agg('mode', axis='columns', numeric_only=True)
# because there can be multiple columns if a row has more than one
# mode
'pandas.core.frame.DataFrame.mode': [
"df.mode(axis='columns', numeric_only=True)"
],
},
not_implemented_ok={
'pandas.core.frame.DataFrame.isin': ['*'],
'pandas.core.frame.DataFrame.melt': ['*'],
'pandas.core.frame.DataFrame.axes': ['*'],
'pandas.core.frame.DataFrame.count': ['*'],
'pandas.core.frame.DataFrame.reindex': ['*'],
'pandas.core.frame.DataFrame.reindex_axis': ['*'],
# We should be able to support pivot and pivot_table for categorical
# columns
'pandas.core.frame.DataFrame.pivot': ['*'],
# DataFrame.__getitem__ cannot be used as loc
'pandas.core.frame.DataFrame.query': [
'df[df.A > df.B]', "df[df.B == df['C C']]"
],
# We can implement this as a zipping operator, but it won't have the
# same capability. The doctest includes an example that branches on
# a deferred result.
'pandas.core.frame.DataFrame.combine': ['*'],
# Can be implemented as a zipping operator
'pandas.core.frame.DataFrame.combine_first': ['*'],
# Difficult to parallelize but should be possible?
'pandas.core.frame.DataFrame.corr': ['*'],
'pandas.core.frame.DataFrame.cov': ['*'],
'pandas.core.frame.DataFrame.dot': ['*'],
# element-wise
'pandas.core.frame.DataFrame.eval': ['*'],
'pandas.core.frame.DataFrame.explode': ['*'],
# Trivially elementwise for axis=columns. Relies on global indexing
# for axis=rows.
'pandas.core.frame.DataFrame.drop': ['*'],
'pandas.core.frame.DataFrame.rename': ['*'],
'pandas.core.frame.DataFrame.apply': ['*'],
# Zipping operation if input is a DeferredSeries
'pandas.core.frame.DataFrame.assign': ['*'],
# In theory this is possible for bounded inputs?
'pandas.core.frame.DataFrame.append': ['*'],
},
skip={
'pandas.core.frame.DataFrame.compare': ['*'],
'pandas.core.frame.DataFrame.drop_duplicates': ['*'],
'pandas.core.frame.DataFrame.duplicated': ['*'],
'pandas.core.frame.DataFrame.groupby': [
'df.groupby(level=0).mean()',
'df.groupby(level="Type").mean()',
'df.groupby(by=["b"], dropna=False).sum()',
'df.groupby(by="a", dropna=False).sum()'
],
'pandas.core.frame.DataFrame.idxmax': ['*'],
'pandas.core.frame.DataFrame.idxmin': ['*'],
'pandas.core.frame.DataFrame.pop': ['*'],
'pandas.core.frame.DataFrame.set_axis': ['*'],
'pandas.core.frame.DataFrame.sort_index': ['*'],
'pandas.core.frame.DataFrame.to_markdown': ['*'],
'pandas.core.frame.DataFrame.to_parquet': ['*'],
'pandas.core.frame.DataFrame.value_counts': ['*'],
'pandas.core.frame.DataFrame.to_records': [
'df.index = df.index.rename("I")',
'index_dtypes = f"<S{df.index.str.len().max()}"', # 1.x
'index_dtypes = "<S{}".format(df.index.str.len().max())', #0.x
'df.to_records(index_dtypes=index_dtypes)',
],
# These tests use the static method pd.pivot_table, which doesn't
# actually raise NotImplementedError
'pandas.core.frame.DataFrame.pivot_table': ['*'],
# Expected to raise a ValueError, but we raise NotImplementedError
'pandas.core.frame.DataFrame.pivot': [
"df.pivot(index='foo', columns='bar', values='baz')"
],
'pandas.core.frame.DataFrame.append': [
'df',
# pylint: disable=line-too-long
"pd.concat([pd.DataFrame([i], columns=['A']) for i in range(5)],\n"
" ignore_index=True)"
],
'pandas.core.frame.DataFrame.eval': ['df'],
# No override for __matmul__ and friends
'pandas.core.frame.DataFrame.dot': ['df @ other'],
'pandas.core.frame.DataFrame.melt': [
"df.columns = [list('ABC'), list('DEF')]", "df"
],
'pandas.core.frame.DataFrame.merge': [
# Order-sensitive index, checked in frames_test.py.
"df1.merge(df2, left_on='lkey', right_on='rkey')",
"df1.merge(df2, left_on='lkey', right_on='rkey',\n"
" suffixes=('_left', '_right'))",
],
# Raises right exception, but testing framework has matching issues.
'pandas.core.frame.DataFrame.replace': [
"df.replace({'a string': 'new value', True: False}) # raises"
],
# Should raise WontImplement order-sensitive
'pandas.core.frame.DataFrame.set_index': [
"df.set_index([pd.Index([1, 2, 3, 4]), 'year'])",
"df.set_index([s, s**2])",
],
'pandas.core.frame.DataFrame.to_sparse': ['type(df)'],
# DeferredSeries has no attribute dtype. Should we allow this and
# defer to proxy?
'pandas.core.frame.DataFrame.iterrows': ["print(df['int'].dtype)"],
# Skipped because "seen_wont_implement" is reset before getting to
# these calls, so the NameError they raise is not ignored.
'pandas.core.frame.DataFrame.T': [
'df1_transposed.dtypes', 'df2_transposed.dtypes'
],
'pandas.core.frame.DataFrame.transpose': [
'df1_transposed.dtypes', 'df2_transposed.dtypes'
],
})
self.assertEqual(result.failed, 0)
def test_dataframe_tests(self):
result = doctests.testmod(
pd.core.frame,
use_beam=False,
report=True,
wont_implement_ok={
'pandas.core.frame.DataFrame.T': ['*'],
'pandas.core.frame.DataFrame.cummax': ['*'],
'pandas.core.frame.DataFrame.cummin': ['*'],
'pandas.core.frame.DataFrame.cumsum': ['*'],
'pandas.core.frame.DataFrame.cumprod': ['*'],
'pandas.core.frame.DataFrame.diff': ['*'],
'pandas.core.frame.DataFrame.items': ['*'],
'pandas.core.frame.DataFrame.itertuples': ['*'],
'pandas.core.frame.DataFrame.iterrows': ['*'],
'pandas.core.frame.DataFrame.iteritems': ['*'],
# default keep is 'first'
'pandas.core.frame.DataFrame.nlargest': [
"df.nlargest(3, 'population')",
"df.nlargest(3, ['population', 'GDP'])",
"df.nlargest(3, 'population', keep='last')"
],
'pandas.core.frame.DataFrame.nsmallest': [
"df.nsmallest(3, 'population')",
"df.nsmallest(3, ['population', 'GDP'])",
"df.nsmallest(3, 'population', keep='last')",
],
'pandas.core.frame.DataFrame.nunique': ['*'],
'pandas.core.frame.DataFrame.to_records': ['*'],
'pandas.core.frame.DataFrame.to_dict': ['*'],
'pandas.core.frame.DataFrame.to_numpy': ['*'],
'pandas.core.frame.DataFrame.to_string': ['*'],
'pandas.core.frame.DataFrame.transpose': ['*'],
'pandas.core.frame.DataFrame.shape': ['*'],
'pandas.core.frame.DataFrame.shift': [
'df.shift(periods=3, freq="D")',
'df.shift(periods=3, freq="infer")'
],
'pandas.core.frame.DataFrame.unstack': ['*'],
'pandas.core.frame.DataFrame.memory_usage': ['*'],
},
skip={
'pandas.core.frame.DataFrame.T':
['df1_transposed.dtypes', 'df2_transposed.dtypes'],
'pandas.core.frame.DataFrame.agg': ['*'],
'pandas.core.frame.DataFrame.aggregate': ['*'],
'pandas.core.frame.DataFrame.append': ['*'],
'pandas.core.frame.DataFrame.apply': ['*'],
'pandas.core.frame.DataFrame.applymap': ['df ** 2'],
'pandas.core.frame.DataFrame.assign': ['*'],
'pandas.core.frame.DataFrame.axes': ['*'],
'pandas.core.frame.DataFrame.combine': ['*'],
'pandas.core.frame.DataFrame.combine_first': ['*'],
'pandas.core.frame.DataFrame.compare': ['*'],
'pandas.core.frame.DataFrame.corr': ['*'],
'pandas.core.frame.DataFrame.count': ['*'],
'pandas.core.frame.DataFrame.cov': ['*'],
'pandas.core.frame.DataFrame.dot': ['*'],
'pandas.core.frame.DataFrame.drop': ['*'],
'pandas.core.frame.DataFrame.drop_duplicates': ['*'],
'pandas.core.frame.DataFrame.duplicated': ['*'],
'pandas.core.frame.DataFrame.eval': ['*'],
'pandas.core.frame.DataFrame.explode': ['*'],
'pandas.core.frame.DataFrame.groupby': [
# More keyword arguments.
'df.groupby(level=0).mean()',
'df.groupby(level="Type").mean()',
'df.groupby(by=["b"], dropna=False).sum()',
'df.groupby(by="a", dropna=False).sum()'
],
'pandas.core.frame.DataFrame.idxmax': ['*'],
'pandas.core.frame.DataFrame.idxmin': ['*'],
'pandas.core.frame.DataFrame.info': ['*'],
'pandas.core.frame.DataFrame.isin': ['*'],
'pandas.core.frame.DataFrame.iterrows':
["print(df['int'].dtype)"],
'pandas.core.frame.DataFrame.melt': ['*'],
'pandas.core.frame.DataFrame.memory_usage': ['*'],
'pandas.core.frame.DataFrame.merge': [
# Order-sensitive index, checked in frames_test.py.
"df1.merge(df2, left_on='lkey', right_on='rkey')",
"df1.merge(df2, left_on='lkey', right_on='rkey',\n"
" suffixes=('_left', '_right'))",
],
# Not equal to df.agg('mode', axis='columns', numeric_only=True)
'pandas.core.frame.DataFrame.mode':
["df.mode(axis='columns', numeric_only=True)"],
'pandas.core.frame.DataFrame.pivot': ['*'],
'pandas.core.frame.DataFrame.pivot_table': ['*'],
'pandas.core.frame.DataFrame.pop': ['*'],
'pandas.core.frame.DataFrame.query': ['*'],
'pandas.core.frame.DataFrame.reindex': ['*'],
# Sets df.index
'pandas.core.frame.DataFrame.reindex_axis': ['*'],
'pandas.core.frame.DataFrame.rename': ['*'],
# Raises right exception, but testing framework has matching issues.
'pandas.core.frame.DataFrame.replace': [
"df.replace({'a string': 'new value', True: False}) # raises"
],
# Uses unseeded np.random.
'pandas.core.frame.DataFrame.round': ['*'],
'pandas.core.frame.DataFrame.set_axis': ['*'],
'pandas.core.frame.DataFrame.set_index': ['*'],
'pandas.core.frame.DataFrame.sort_index': ['*'],
'pandas.core.frame.DataFrame.transpose':
['df1_transposed.dtypes', 'df2_transposed.dtypes'],
'pandas.core.frame.DataFrame.to_markdown': ['*'],
'pandas.core.frame.DataFrame.to_parquet': ['*'],
# Uses df.index
'pandas.core.frame.DataFrame.to_records': ['*'],
'pandas.core.frame.DataFrame.to_sparse': ['type(df)'],
'pandas.core.frame.DataFrame.value_counts': ['*'],
})
self.assertEqual(result.failed, 0)
def test_series_tests(self):
result = doctests.testmod(
pd.core.series,
use_beam=False,
report=True,
wont_implement_ok={
'pandas.core.series.Series.__array__': ['*'],
'pandas.core.series.Series.cummax': ['*'],
'pandas.core.series.Series.cummin': ['*'],
'pandas.core.series.Series.cumsum': ['*'],
'pandas.core.series.Series.cumprod': ['*'],
'pandas.core.series.Series.diff': ['*'],
'pandas.core.series.Series.items': ['*'],
'pandas.core.series.Series.iteritems': ['*'],
# default keep is 'first'
'pandas.core.series.Series.nlargest': [
"s.nlargest()",
"s.nlargest(3)",
"s.nlargest(3, keep='last')",
],
'pandas.core.series.Series.memory_usage': ['*'],
'pandas.core.series.Series.nsmallest': [
"s.nsmallest()",
"s.nsmallest(3)",
"s.nsmallest(3, keep='last')",
],
'pandas.core.series.Series.searchsorted': ['*'],
'pandas.core.series.Series.shift': ['*'],
'pandas.core.series.Series.take': ['*'],
'pandas.core.series.Series.to_dict': ['*'],
'pandas.core.series.Series.unique': ['*'],
'pandas.core.series.Series.unstack': ['*'],
'pandas.core.series.Series.values': ['*'],
'pandas.core.series.Series.view': ['*'],
},
not_implemented_ok={
'pandas.core.series.Series.reindex': ['*'],
},
skip={
'pandas.core.series.Series.array': ['*'],
'pandas.core.series.Series.append': ['*'],
'pandas.core.series.Series.argmax': ['*'],
'pandas.core.series.Series.argmin': ['*'],
'pandas.core.series.Series.autocorr': ['*'],
'pandas.core.series.Series.combine': ['*'],
'pandas.core.series.Series.combine_first': ['*'],
'pandas.core.series.Series.compare': ['*'],
'pandas.core.series.Series.corr': ['*'],
'pandas.core.series.Series.count': ['*'],
'pandas.core.series.Series.cov': ['*'],
'pandas.core.series.Series.dot': ['*'],
'pandas.core.series.Series.drop': ['*'],
'pandas.core.series.Series.drop_duplicates': ['*'],
'pandas.core.series.Series.duplicated': ['*'],
'pandas.core.series.Series.explode': ['*'],
'pandas.core.series.Series.groupby': ['*'],
'pandas.core.series.Series.idxmax': ['*'],
'pandas.core.series.Series.idxmin': ['*'],
'pandas.core.series.Series.name': ['*'],
'pandas.core.series.Series.nonzero': ['*'],
'pandas.core.series.Series.pop': ['*'],
'pandas.core.series.Series.quantile': ['*'],
'pandas.core.series.Series.rename': ['*'],
'pandas.core.series.Series.repeat': ['*'],
'pandas.core.series.Series.replace': ['*'],
'pandas.core.series.Series.reset_index': ['*'],
'pandas.core.series.Series.searchsorted': [
# This doctest seems to be incorrectly parsed.
"x = pd.Categorical(['apple', 'bread', 'bread',"
],
'pandas.core.series.Series.set_axis': ['*'],
'pandas.core.series.Series.sort_index': ['*'],
'pandas.core.series.Series.sort_values': ['*'],
'pandas.core.series.Series.to_csv': ['*'],
'pandas.core.series.Series.to_markdown': ['*'],
'pandas.core.series.Series.update': ['*'],
'pandas.core.series.Series.view': [
# Inspection after modification.
's'
],
})
self.assertEqual(result.failed, 0)
def test_groupby_tests(self):
result = doctests.testmod(
pd.core.groupby.groupby,
use_beam=False,
wont_implement_ok={
'pandas.core.groupby.groupby.GroupBy.head': ['*'],
'pandas.core.groupby.groupby.GroupBy.tail': ['*'],
'pandas.core.groupby.groupby.GroupBy.nth': ['*'],
'pandas.core.groupby.groupby.GroupBy.cumcount': ['*'],
'pandas.core.groupby.groupby.GroupBy.resample': ['*'],
},
not_implemented_ok={
'pandas.core.groupby.groupby.GroupBy.ngroup': ['*'],
'pandas.core.groupby.groupby.GroupBy.sample': ['*'],
'pandas.core.groupby.groupby.GroupBy.rank': ['*'],
'pandas.core.groupby.groupby.GroupBy.nth': [
"df.groupby('A', as_index=False).nth(1)",
],
},
skip={
# Uses iloc to mutate a DataFrame
'pandas.core.groupby.groupby.GroupBy.resample': [
'df.iloc[2, 0] = 5',
'df',
],
# df is reassigned
'pandas.core.groupby.groupby.GroupBy.rank': ['df'],
# TODO: Raise wont implement for list passed as a grouping column
# Currently raises unhashable type: list
'pandas.core.groupby.groupby.GroupBy.ngroup': [
'df.groupby(["A", [1,1,2,3,2,1]]).ngroup()'
],
})
self.assertEqual(result.failed, 0)
result = doctests.testmod(
pd.core.groupby.generic,
use_beam=False,
wont_implement_ok={
# Returns an array by default, not a Series. WontImplement
# (non-deferred)
'pandas.core.groupby.generic.SeriesGroupBy.unique': ['*'],
# TODO: Is take actually deprecated?
'pandas.core.groupby.generic.DataFrameGroupBy.take': ['*'],
'pandas.core.groupby.generic.SeriesGroupBy.take': ['*'],
'pandas.core.groupby.generic.SeriesGroupBy.nsmallest': [
"s.nsmallest(3, keep='last')",
"s.nsmallest(3)",
"s.nsmallest()",
],
'pandas.core.groupby.generic.SeriesGroupBy.nlargest': [
"s.nlargest(3, keep='last')",
"s.nlargest(3)",
"s.nlargest()",
],
'pandas.core.groupby.generic.DataFrameGroupBy.diff': ['*'],
'pandas.core.groupby.generic.SeriesGroupBy.diff': ['*'],
'pandas.core.groupby.generic.DataFrameGroupBy.hist': ['*'],
'pandas.core.groupby.generic.DataFrameGroupBy.fillna': [
'df.fillna(method=\'ffill\')',
'df.fillna(method="ffill")',
'df.fillna(value=values, limit=1)',
],
'pandas.core.groupby.generic.SeriesGroupBy.fillna': [
'df.fillna(method=\'ffill\')',
'df.fillna(method="ffill")',
'df.fillna(value=values, limit=1)',
],
},
not_implemented_ok={
'pandas.core.groupby.generic.DataFrameGroupBy.idxmax': ['*'],
'pandas.core.groupby.generic.DataFrameGroupBy.idxmin': ['*'],
'pandas.core.groupby.generic.SeriesGroupBy.transform': ['*'],
'pandas.core.groupby.generic.SeriesGroupBy.idxmax': ['*'],
'pandas.core.groupby.generic.SeriesGroupBy.idxmin': ['*'],
'pandas.core.groupby.generic.SeriesGroupBy.apply': ['*'],
},
skip={
'pandas.core.groupby.generic.SeriesGroupBy.cov': [
# Floating point comparison fails
's1.cov(s2)',
],
'pandas.core.groupby.generic.DataFrameGroupBy.cov': [
# Mutates input DataFrame with loc
# TODO: Replicate in frames_test.py
"df.loc[df.index[:5], 'a'] = np.nan",
"df.loc[df.index[5:10], 'b'] = np.nan",
"df.cov(min_periods=12)",
],
# These examples rely on grouping by a list
'pandas.core.groupby.generic.SeriesGroupBy.aggregate': ['*'],
'pandas.core.groupby.generic.DataFrameGroupBy.aggregate': ['*'],
'pandas.core.groupby.generic.SeriesGroupBy.transform': [
# Dropping invalid columns during a transform is unsupported.
'grouped.transform(lambda x: (x - x.mean()) / x.std())'
],
'pandas.core.groupby.generic.DataFrameGroupBy.transform': [
# Dropping invalid columns during a transform is unsupported.
'grouped.transform(lambda x: (x - x.mean()) / x.std())'
],
# Skipped idxmax/idxmin due an issue with the test framework
'pandas.core.groupby.generic.SeriesGroupBy.idxmin': ['s.idxmin()'],
'pandas.core.groupby.generic.SeriesGroupBy.idxmax': ['s.idxmax()'],
# Uses as_index, which is currently not_implemented
'pandas.core.groupby.generic.DataFrameGroupBy.value_counts': [
"df.groupby('gender', as_index=False).value_counts()",
# pylint: disable=line-too-long
"df.groupby('gender', as_index=False).value_counts(normalize=True)",
],
})
self.assertEqual(result.failed, 0)
def test_dataframe_tests(self):
result = doctests.testmod(
pd.core.frame,
use_beam=False,
report=True,
wont_implement_ok={
'pandas.core.frame.DataFrame.T': ['*'],
'pandas.core.frame.DataFrame.cummax': ['*'],
'pandas.core.frame.DataFrame.cummin': ['*'],
'pandas.core.frame.DataFrame.cumsum': ['*'],
'pandas.core.frame.DataFrame.cumprod': ['*'],
'pandas.core.frame.DataFrame.diff': ['*'],
'pandas.core.frame.DataFrame.fillna': [
'df.fillna(method=\'ffill\')',
'df.fillna(method="ffill")',
'df.fillna(value=values, limit=1)',
],
'pandas.core.frame.DataFrame.items': ['*'],
'pandas.core.frame.DataFrame.itertuples': ['*'],
'pandas.core.frame.DataFrame.iterrows': ['*'],
'pandas.core.frame.DataFrame.iteritems': ['*'],
# default keep is 'first'
'pandas.core.frame.DataFrame.nlargest': [
"df.nlargest(3, 'population')",
"df.nlargest(3, ['population', 'GDP'])",
"df.nlargest(3, 'population', keep='last')"
],
'pandas.core.frame.DataFrame.nsmallest': [
"df.nsmallest(3, 'population')",
"df.nsmallest(3, ['population', 'GDP'])",
"df.nsmallest(3, 'population', keep='last')",
],
'pandas.core.frame.DataFrame.replace': [
"s.replace([1, 2], method='bfill')",
# Relies on method='pad'
"s.replace('a')",
# Relies on method='pad'
# value=None is not valid for pandas < 1.4
"s.replace('a', None)",
# Implicitly uses method='pad', but output doesn't rely on that
# behavior. Verified indepently in
# frames_test.py::DeferredFrameTest::test_replace
"df.replace(regex={r'^ba.$': 'new', 'foo': 'xyz'})"
],
'pandas.core.frame.DataFrame.to_records': ['*'],
'pandas.core.frame.DataFrame.to_dict': ['*'],
'pandas.core.frame.DataFrame.to_numpy': ['*'],
'pandas.core.frame.DataFrame.to_string': ['*'],
'pandas.core.frame.DataFrame.transpose': ['*'],
'pandas.core.frame.DataFrame.shape': ['*'],
'pandas.core.frame.DataFrame.shift': [
'df.shift(periods=3)',
'df.shift(periods=3, fill_value=0)',
],
'pandas.core.frame.DataFrame.unstack': ['*'],
'pandas.core.frame.DataFrame.memory_usage': ['*'],
'pandas.core.frame.DataFrame.info': ['*'],
# Not equal to df.agg('mode', axis='columns', numeric_only=True)
# because there can be multiple columns if a row has more than one
# mode
'pandas.core.frame.DataFrame.mode': [
"df.mode(axis='columns', numeric_only=True)"
],
'pandas.core.frame.DataFrame.append': [
'df.append(df2, ignore_index=True)',
"for i in range(5):\n" +
" df = df.append({'A': i}, ignore_index=True)",
],
'pandas.core.frame.DataFrame.sort_index': ['*'],
'pandas.core.frame.DataFrame.sort_values': ['*'],
'pandas.core.frame.DataFrame.melt': [
"df.melt(id_vars=['A'], value_vars=['B'])",
"df.melt(id_vars=['A'], value_vars=['B', 'C'])",
"df.melt(col_level=0, id_vars=['A'], value_vars=['B'])",
"df.melt(id_vars=[('A', 'D')], value_vars=[('B', 'E')])",
"df.melt(id_vars=['A'], value_vars=['B'],\n" +
" var_name='myVarname', value_name='myValname')"
],
# Most keep= options are order-sensitive
'pandas.core.frame.DataFrame.drop_duplicates': ['*'],
'pandas.core.frame.DataFrame.duplicated': [
'df.duplicated()',
"df.duplicated(keep='last')",
"df.duplicated(subset=['brand'])",
],
'pandas.core.frame.DataFrame.reindex': ['*'],
'pandas.core.frame.DataFrame.dot': [
# reindex not supported
's2 = s.reindex([1, 0, 2, 3])',
],
'pandas.core.frame.DataFrame.resample': ['*'],
'pandas.core.frame.DataFrame.values': ['*'],
},
not_implemented_ok={
'pandas.core.frame.DataFrame.transform': [
# str arg not supported. Tested with np.sum in
# frames_test.py::DeferredFrameTest::test_groupby_transform_sum
"df.groupby('Date')['Data'].transform('sum')",
],
'pandas.core.frame.DataFrame.melt': ['*'],
'pandas.core.frame.DataFrame.reindex_axis': ['*'],
'pandas.core.frame.DataFrame.round': [
'df.round(decimals)',
],
# We should be able to support pivot and pivot_table for categorical
# columns
'pandas.core.frame.DataFrame.pivot': ['*'],
# Trivially elementwise for axis=columns. Relies on global indexing
# for axis=rows.
# Difficult to determine proxy, need to inspect function
'pandas.core.frame.DataFrame.apply': ['*'],
# Cross-join not implemented
'pandas.core.frame.DataFrame.merge': [
"df1.merge(df2, how='cross')"
],
# TODO(BEAM-11711)
'pandas.core.frame.DataFrame.set_index': [
"df.set_index([s, s**2])",
],
'pandas.core.frame.DataFrame.set_axis': [
"df.set_axis(range(0,2), axis='index')",
],
# TODO(BEAM-12495)
'pandas.core.frame.DataFrame.value_counts': [
'df.value_counts(dropna=False)'
],
},
skip={
# DataFrame construction from a dictionary and
# Series requires using the len() function, which
# is a non-deferred operation that we do not allow
'pandas.core.frame.DataFrame': [
'pd.DataFrame(data=d, index=[0, 1, 2, 3])',
],
# s2 created with reindex
'pandas.core.frame.DataFrame.dot': [
'df.dot(s2)',
],
'pandas.core.frame.DataFrame.resample': ['df'],
'pandas.core.frame.DataFrame.asfreq': ['*'],
# Throws NotImplementedError when modifying df
'pandas.core.frame.DataFrame.axes': [
# Returns deferred index.
'df.axes',
],
# Skipped because the relies on loc to set cells in df2
'pandas.core.frame.DataFrame.compare': ['*'],
'pandas.core.frame.DataFrame.cov': [
# Relies on setting entries ahead of time.
"df.loc[df.index[:5], 'a'] = np.nan",
"df.loc[df.index[5:10], 'b'] = np.nan",
'df.cov(min_periods=12)',
],
'pandas.core.frame.DataFrame.rename': [
# Returns deferred index.
'df.index',
'df.rename(index=str).index',
],
'pandas.core.frame.DataFrame.set_index': [
# TODO(BEAM-11711): This could pass in the index as
# a DeferredIndex, and we should fail it as order-sensitive.
"df.set_index([pd.Index([1, 2, 3, 4]), 'year'])",
],
'pandas.core.frame.DataFrame.set_axis': [
# This should pass as set_axis(axis='columns')
# and fail with set_axis(axis='index')
"df.set_axis(['a', 'b', 'c'], axis='index')"
],
'pandas.core.frame.DataFrame.to_markdown': ['*'],
'pandas.core.frame.DataFrame.to_parquet': ['*'],
# Raises right exception, but testing framework has matching issues.
# Tested in `frames_test.py`.
'pandas.core.frame.DataFrame.insert': [
'df',
'df.insert(1, "newcol", [99, 99])',
'df.insert(0, "col1", [100, 100], allow_duplicates=True)'
],
'pandas.core.frame.DataFrame.to_records': [
'df.index = df.index.rename("I")',
'index_dtypes = f"<S{df.index.str.len().max()}"', # 1.x
'index_dtypes = "<S{}".format(df.index.str.len().max())', #0.x
'df.to_records(index_dtypes=index_dtypes)',
],
# These tests use the static method pd.pivot_table, which doesn't
# actually raise NotImplementedError
'pandas.core.frame.DataFrame.pivot_table': ['*'],
# Expected to raise a ValueError, but we raise NotImplementedError
'pandas.core.frame.DataFrame.pivot': [
"df.pivot(index='foo', columns='bar', values='baz')"
],
'pandas.core.frame.DataFrame.append': [
'df',
# pylint: disable=line-too-long
"pd.concat([pd.DataFrame([i], columns=['A']) for i in range(5)],\n"
" ignore_index=True)"
],
'pandas.core.frame.DataFrame.eval': ['df'],
'pandas.core.frame.DataFrame.melt': [
"df.columns = [list('ABC'), list('DEF')]", "df"
],
'pandas.core.frame.DataFrame.merge': [
# Order-sensitive index, checked in frames_test.py.
"df1.merge(df2, left_on='lkey', right_on='rkey')",
"df1.merge(df2, left_on='lkey', right_on='rkey',\n"
" suffixes=('_left', '_right'))",
"df1.merge(df2, how='left', on='a')",
],
# Raises right exception, but testing framework has matching issues.
'pandas.core.frame.DataFrame.replace': [
"df.replace({'a string': 'new value', True: False}) # raises"
],
'pandas.core.frame.DataFrame.to_sparse': ['type(df)'],
# Skipped because "seen_wont_implement" is reset before getting to
# these calls, so the NameError they raise is not ignored.
'pandas.core.frame.DataFrame.T': [
'df1_transposed.dtypes', 'df2_transposed.dtypes'
],
'pandas.core.frame.DataFrame.transpose': [
'df1_transposed.dtypes', 'df2_transposed.dtypes'
],
# Skipped because the relies on iloc to set a cell to NA. Test is
# replicated in frames_test::DeferredFrameTest::test_applymap.
'pandas.core.frame.DataFrame.applymap': [
'df_copy.iloc[0, 0] = pd.NA',
"df_copy.applymap(lambda x: len(str(x)), na_action='ignore')",
],
# Skipped so we don't need to install natsort
'pandas.core.frame.DataFrame.sort_values': [
'from natsort import index_natsorted',
'df.sort_values(\n'
' by="time",\n'
' key=lambda x: np.argsort(index_natsorted(df["time"]))\n'
')'
],
# Mode that we don't yet support, documentation added in pandas
# 1.2.0 (https://github.com/pandas-dev/pandas/issues/35912)
'pandas.core.frame.DataFrame.aggregate': [
"df.agg(x=('A', max), y=('B', 'min'), z=('C', np.mean))"
],
})
self.assertEqual(result.failed, 0)
def test_string_tests(self):
# TODO(BEAM-10720)
result = doctests.testmod(
pd.core.strings,
use_beam=False,
skip={
'pandas.core.strings.StringMethods': ['*'],
'pandas.core.strings.StringMethods.capitalize': ['*'],
'pandas.core.strings.StringMethods.casefold': ['*'],
'pandas.core.strings.StringMethods.cat': ['*'],
'pandas.core.strings.StringMethods.contains': ['*'],
'pandas.core.strings.StringMethods.count': ['*'],
'pandas.core.strings.StringMethods.endswith': ['*'],
'pandas.core.strings.StringMethods.extract': ['*'],
'pandas.core.strings.StringMethods.extractall': ['*'],
'pandas.core.strings.StringMethods.findall': ['*'],
'pandas.core.strings.StringMethods.get': ['*'],
'pandas.core.strings.StringMethods.get_dummies': ['*'],
'pandas.core.strings.StringMethods.isalnum': ['*'],
'pandas.core.strings.StringMethods.isalpha': ['*'],
'pandas.core.strings.StringMethods.isdecimal': ['*'],
'pandas.core.strings.StringMethods.isdigit': ['*'],
'pandas.core.strings.StringMethods.islower': ['*'],
'pandas.core.strings.StringMethods.isnumeric': ['*'],
'pandas.core.strings.StringMethods.isspace': ['*'],
'pandas.core.strings.StringMethods.istitle': ['*'],
'pandas.core.strings.StringMethods.isupper': ['*'],
'pandas.core.strings.StringMethods.join': ['*'],
'pandas.core.strings.StringMethods.len': ['*'],
'pandas.core.strings.StringMethods.lower': ['*'],
'pandas.core.strings.StringMethods.lstrip': ['*'],
'pandas.core.strings.StringMethods.pad': ['*'],
'pandas.core.strings.StringMethods.partition': ['*'],
'pandas.core.strings.StringMethods.repeat': ['*'],
'pandas.core.strings.StringMethods.replace': ['*'],
'pandas.core.strings.StringMethods.rpartition': ['*'],
'pandas.core.strings.StringMethods.rsplit': ['*'],
'pandas.core.strings.StringMethods.rstrip': ['*'],
'pandas.core.strings.StringMethods.slice': ['*'],
'pandas.core.strings.StringMethods.slice_replace': ['*'],
'pandas.core.strings.StringMethods.split': ['*'],
'pandas.core.strings.StringMethods.startswith': ['*'],
'pandas.core.strings.StringMethods.strip': ['*'],
'pandas.core.strings.StringMethods.swapcase': ['*'],
'pandas.core.strings.StringMethods.title': ['*'],
'pandas.core.strings.StringMethods.upper': ['*'],
'pandas.core.strings.StringMethods.wrap': ['*'],
'pandas.core.strings.StringMethods.zfill': ['*'],
'pandas.core.strings.str_contains': ['*'],
'pandas.core.strings.str_count': ['*'],
'pandas.core.strings.str_endswith': ['*'],
'pandas.core.strings.str_extract': ['*'],
'pandas.core.strings.str_extractall': ['*'],
'pandas.core.strings.str_findall': ['*'],
'pandas.core.strings.str_get': ['*'],
'pandas.core.strings.str_get_dummies': ['*'],
'pandas.core.strings.str_join': ['*'],
'pandas.core.strings.str_pad': ['*'],
'pandas.core.strings.str_repeat': ['*'],
'pandas.core.strings.str_replace': ['*'],
'pandas.core.strings.str_slice': ['*'],
'pandas.core.strings.str_slice_replace': ['*'],
'pandas.core.strings.str_startswith': ['*'],
'pandas.core.strings.str_wrap': ['*'],
})
self.assertEqual(result.failed, 0)
def test_series_tests(self):
result = doctests.testmod(
pd.core.series,
use_beam=False,
report=True,
wont_implement_ok={
'pandas.core.series.Series.__array__': ['*'],
'pandas.core.series.Series.array': ['*'],
'pandas.core.series.Series.cummax': ['*'],
'pandas.core.series.Series.cummin': ['*'],
'pandas.core.series.Series.cumsum': ['*'],
'pandas.core.series.Series.cumprod': ['*'],
'pandas.core.series.Series.diff': ['*'],
'pandas.core.series.Series.dot': [
's.dot(arr)', # non-deferred result
],
'pandas.core.series.Series.fillna': [
'df.fillna(method=\'ffill\')',
'df.fillna(method="ffill")',
'df.fillna(value=values, limit=1)',
],
'pandas.core.series.Series.info': ['*'],
'pandas.core.series.Series.items': ['*'],
'pandas.core.series.Series.iteritems': ['*'],
# default keep is 'first'
'pandas.core.series.Series.nlargest': [
"s.nlargest()",
"s.nlargest(3)",
"s.nlargest(3, keep='last')",
],
'pandas.core.series.Series.memory_usage': ['*'],
'pandas.core.series.Series.nsmallest': [
"s.nsmallest()",
"s.nsmallest(3)",
"s.nsmallest(3, keep='last')",
],
'pandas.core.series.Series.pop': ['*'],
'pandas.core.series.Series.searchsorted': ['*'],
'pandas.core.series.Series.shift': [
'df.shift(periods=3)',
'df.shift(periods=3, fill_value=0)',
],
'pandas.core.series.Series.take': ['*'],
'pandas.core.series.Series.to_dict': ['*'],
'pandas.core.series.Series.unique': ['*'],
'pandas.core.series.Series.unstack': ['*'],
'pandas.core.series.Series.values': ['*'],
'pandas.core.series.Series.view': ['*'],
'pandas.core.series.Series.append': [
's1.append(s2, ignore_index=True)',
],
'pandas.core.series.Series.replace': [
"s.replace([1, 2], method='bfill')",
# Relies on method='pad'
"s.replace('a')",
# Relies on method='pad'
# value=None is not valid for pandas < 1.4
"s.replace('a', None)",
# Implicitly uses method='pad', but output doesn't rely on that
# behavior. Verified indepently in
# frames_test.py::DeferredFrameTest::test_replace
"df.replace(regex={r'^ba.$': 'new', 'foo': 'xyz'})"
],
'pandas.core.series.Series.sort_index': ['*'],
'pandas.core.series.Series.sort_values': ['*'],
'pandas.core.series.Series.argmax': ['*'],
'pandas.core.series.Series.argmin': ['*'],
'pandas.core.series.Series.drop_duplicates': [
's.drop_duplicates()',
"s.drop_duplicates(keep='last')",
],
'pandas.core.series.Series.reindex': ['*'],
'pandas.core.series.Series.autocorr': ['*'],
'pandas.core.series.Series.repeat': ['s.repeat([1, 2, 3])'],
'pandas.core.series.Series.resample': ['*'],
'pandas.core.series.Series': ['ser.iloc[0] = 999'],
},
not_implemented_ok={
'pandas.core.series.Series.transform': [
# str arg not supported. Tested with np.sum in
# frames_test.py::DeferredFrameTest::test_groupby_transform_sum
"df.groupby('Date')['Data'].transform('sum')",
],
'pandas.core.series.Series.groupby': [
'ser.groupby(["a", "b", "a", "b"]).mean()',
'ser.groupby(["a", "b", "a", np.nan]).mean()',
'ser.groupby(["a", "b", "a", np.nan], dropna=False).mean()',
],
},
skip={
# Relies on setting values with iloc
'pandas.core.series.Series': ['ser', 'r'],
'pandas.core.series.Series.groupby': [
# TODO(BEAM-11393): This example requires aligning two series
# with non-unique indexes. It only works in pandas because
# pandas can recognize the indexes are identical and elide the
# alignment.
'ser.groupby(ser > 100).mean()',
],
'pandas.core.series.Series.asfreq': ['*'],
# error formatting
'pandas.core.series.Series.append': [
's1.append(s2, verify_integrity=True)',
],
'pandas.core.series.Series.cov': [
# Differs in LSB on jenkins.
"s1.cov(s2)",
],
# Skipped idxmax/idxmin due an issue with the test framework
'pandas.core.series.Series.idxmin': ['s.idxmin()'],
'pandas.core.series.Series.idxmax': ['s.idxmax()'],
'pandas.core.series.Series.duplicated': ['*'],
'pandas.core.series.Series.set_axis': ['*'],
'pandas.core.series.Series.nonzero': ['*'],
'pandas.core.series.Series.pop': ['ser'], # testing side effect
# Raises right exception, but testing framework has matching issues.
'pandas.core.series.Series.replace': [
"df.replace({'a string': 'new value', True: False}) # raises"
],
'pandas.core.series.Series.searchsorted': [
# This doctest seems to be incorrectly parsed.
"x = pd.Categorical(['apple', 'bread', 'bread',"
],
'pandas.core.series.Series.to_csv': ['*'],
'pandas.core.series.Series.to_markdown': ['*'],
'pandas.core.series.Series.update': ['*'],
'pandas.core.series.Series.view': [
# Inspection after modification.
's'
],
'pandas.core.series.Series.resample': ['df'],
})
self.assertEqual(result.failed, 0)
