#

# Licensed to the Apache Software Foundation (ASF) under one or more

# contributor license agreements. See the NOTICE file distributed with

# this work for additional information regarding copyright ownership.

# The ASF licenses this file to You under the Apache License, Version 2.0

# (the "License"); you may not use this file except in compliance with

# the License. You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import re

import sys

import unittest

import warnings

import numpy as np

import pandas as pd

import pytest

from parameterized import parameterized

import apache_beam as beam

from apache_beam.dataframe import expressions

from apache_beam.dataframe import frame_base

from apache_beam.dataframe import frames

from apache_beam.dataframe.convert import to_dataframe

from apache_beam.dataframe.doctests import teststring

from apache_beam.runners.interactive import interactive_beam as ib

from apache_beam.runners.interactive import interactive_environment as ie

from apache_beam.runners.interactive.interactive_runner import InteractiveRunner

from apache_beam.runners.interactive.testing.mock_env import isolated_env

# Get major, minor version

PD_VERSION = tuple(map(int, pd.__version__.split('.')[0:2]))

GROUPBY_DF = pd.DataFrame({

'group': ['a' if i % 5 == 0 or i % 3 == 0 else 'b' for i in range(100)],

'foo': [None if i % 11 == 0 else i for i in range(100)],

'bar': [None if i % 7 == 0 else 99 - i for i in range(100)],

'baz': [None if i % 13 == 0 else i * 2 for i in range(100)],

'bool': [i % 17 == 0 for i in range(100)],

'str': [str(i) for i in range(100)],

})

if PD_VERSION < (2, 0):

# All these are things that are fixed in the Pandas 2 transition.

pytestmark = pytest.mark.filterwarnings("ignore::FutureWarning")

def _get_deferred_args(*args):

return [

frame_base.DeferredFrame.wrap(

expressions.ConstantExpression(arg, arg[0:0])) for arg in args

]

class _AbstractFrameTest(unittest.TestCase):

"""Test sub-class with utilities for verifying DataFrame operations."""

def _run_error_test(

self, func, *args, construction_time=True, distributed=True):

"""Verify that func(*args) raises the same exception in pandas and in Beam.

Note that by default this only checks for exceptions that the Beam DataFrame

API raises during expression generation (i.e. construction time).

Exceptions raised while the pipeline is executing are less helpful, but

are sometimes unavoidable (e.g. data validation exceptions), to check for

these exceptions use construction_time=False."""

deferred_args = _get_deferred_args(*args)

# Get expected error

try:

expected = func(*args)

except Exception as e:

expected_error = e

else:

raise AssertionError(

"Expected an error, but executing with pandas successfully "

f"returned:\n{expected}")

# Get actual error

if construction_time:

try:

_ = func(*deferred_args)._expr

except Exception as e:

actual = e

else:

raise AssertionError(

f"Expected an error:\n{expected_error}\nbut Beam successfully "

f"generated an expression.")

else: # not construction_time

# Check for an error raised during pipeline execution

expr = func(*deferred_args)._expr

session_type = (

expressions.PartitioningSession

if distributed else expressions.Session)

try:

result = session_type({}).evaluate(expr)

except Exception as e:

actual = e

else:

raise AssertionError(

f"Expected an error:\n{expected_error}\nbut Beam successfully "

f"Computed the result:\n{result}.")

# Verify

if (not isinstance(actual, type(expected_error)) or

str(expected_error) not in str(actual)):

raise AssertionError(

f'Expected {expected_error!r} to be raised, but got {actual!r}'

) from actual

def _run_inplace_test(self, func, arg, **kwargs):

"""Verify an inplace operation performed by func.

Checks that func performs the same inplace operation on arg, in pandas and

in Beam."""

def wrapper(df):

df = df.copy()

func(df)

return df

self._run_test(wrapper, arg, **kwargs)

def _run_test(

self,

func,

*args,

distributed=True,

nonparallel=False,

check_proxy=True,

lenient_dtype_check=False):

"""Verify that func(*args) produces the same result in pandas and in Beam.

Args:

distributed (bool): Whether or not to use PartitioningSession to

simulate parallel execution.

nonparallel (bool): Whether or not this function contains a

non-parallelizable operation. If True, the expression will be

generated twice, once outside of an allow_non_parallel_operations

block (to verify NonParallelOperation is raised), and again inside

of an allow_non_parallel_operations block to actually generate an

expression to verify.

check_proxy (bool): Whether or not to check that the proxy of the

generated expression matches the actual result, defaults to True.

This option should NOT be set to False in tests added for new

operations if at all possible. Instead make sure the new operation

produces the correct proxy. This flag only exists as an escape hatch

until existing failures can be addressed

(https://github.com/apache/beam/issues/20926).

lenient_dtype_check (bool): Whether or not to check that numeric columns

are still numeric between actual and proxy. i.e. verify that they

are at least int64 or float64, and not necessarily have the exact

same dtype. This may need to be set to True for some non-deferred

operations, where the dtype of the values in the proxy are not known

ahead of time, causing int64 to float64 coercion issues.

"""

# Compute expected value

expected = func(*args)

# Compute actual value

deferred_args = _get_deferred_args(*args)

if nonparallel:

# First run outside a nonparallel block to confirm this raises as expected

with self.assertRaises(expressions.NonParallelOperation) as raised:

func(*deferred_args)

if raised.exception.msg.startswith(

"Encountered non-parallelizable form of"):

raise AssertionError(

"Default NonParallelOperation raised, please specify a reason in "

"the Singleton() partitioning requirement for this operation."

) from raised.exception

# Re-run in an allow non parallel block to get an expression to verify

with beam.dataframe.allow_non_parallel_operations():

expr = func(*deferred_args)._expr

else:

expr = func(*deferred_args)._expr

# Compute the result of the generated expression

session_type = (

expressions.PartitioningSession if distributed else expressions.Session)

actual = session_type({}).evaluate(expr)

# Verify

if isinstance(expected, pd.core.generic.NDFrame):

if distributed:

if expected.index.is_unique:

expected = expected.sort_index()

actual = actual.sort_index()

elif isinstance(expected, pd.Series):

expected = expected.sort_values()

actual = actual.sort_values()

else:

expected = expected.sort_values(list(expected.columns))

actual = actual.sort_values(list(actual.columns))

if isinstance(expected, pd.Series):

if lenient_dtype_check:

pd.testing.assert_series_equal(

expected.astype('Float64'), actual.astype('Float64'))

else:

pd.testing.assert_series_equal(expected, actual)

elif isinstance(expected, pd.DataFrame):

if lenient_dtype_check:

pd.testing.assert_frame_equal(

expected.astype('Float64'), actual.astype('Float64'))

else:

pd.testing.assert_frame_equal(expected, actual)

else:

raise ValueError(

f"Expected value is a {type(expected)},"

"not a Series or DataFrame.")

else:

# Expectation is not a pandas object

if isinstance(expected, float):

if np.isnan(expected):

cmp = np.isnan

else:

cmp = lambda x: np.isclose(expected, x)

else:

cmp = lambda x: x == expected

self.assertTrue(

cmp(actual), 'Expected:\n\n%r\n\nActual:\n\n%r' % (expected, actual))

if check_proxy:

# Verify that the actual result agrees with the proxy

proxy = expr.proxy()

if type(actual) in (np.float32, np.float64):

self.assertTrue(type(actual) == type(proxy) or np.isnan(proxy))

else:

self.assertEqual(type(actual), type(proxy))

if isinstance(expected, pd.core.generic.NDFrame):

if isinstance(expected, pd.Series):

if lenient_dtype_check:

self.assertEqual(

actual.astype('Float64').dtype, proxy.astype('Float64').dtype)

else:

self.assertEqual(actual.dtype, proxy.dtype)

self.assertEqual(actual.name, proxy.name)

elif isinstance(expected, pd.DataFrame):

if lenient_dtype_check:

pd.testing.assert_series_equal(

actual.astype('Float64').dtypes, proxy.astype('Float64').dtypes)

else:

pd.testing.assert_series_equal(actual.dtypes, proxy.dtypes)

else:

raise ValueError(

f"Expected value is a {type(expected)},"

"not a Series or DataFrame.")

self.assertEqual(actual.index.names, proxy.index.names)

for i in range(actual.index.nlevels):

if lenient_dtype_check:

self.assertEqual(

actual.astype('Float64').index.get_level_values(i).dtype,

proxy.astype('Float64').index.get_level_values(i).dtype)

else:

self.assertEqual(

actual.index.get_level_values(i).dtype,

proxy.index.get_level_values(i).dtype)

class DeferredFrameTest(_AbstractFrameTest):

"""Miscellaneous tessts for DataFrame operations."""

def test_series_arithmetic(self):

a = pd.Series([1, 2, 3])

b = pd.Series([100, 200, 300])

self._run_test(lambda a, b: a - 2 * b, a, b)

self._run_test(lambda a, b: a.subtract(2).multiply(b).divide(a), a, b)

def test_dataframe_arithmetic(self):

df = pd.DataFrame({'a': [1, 2, 3], 'b': [100, 200, 300]})

df2 = pd.DataFrame({'a': [3000, 1000, 2000], 'b': [7, 11, 13]})

self._run_test(lambda df, df2: df - 2 * df2, df, df2)

self._run_test(

lambda df, df2: df.subtract(2).multiply(df2).divide(df), df, df2)

@unittest.skipIf(PD_VERSION < (1, 3), "dropna=False is new in pandas 1.3")

def test_value_counts_dropna_false(self):

df = pd.DataFrame({

'first_name': ['John', 'Anne', 'John', 'Beth'],

'middle_name': ['Smith', pd.NA, pd.NA, 'Louise']

})

# TODO(https://github.com/apache/beam/issues/21014): Remove the

# assertRaises this when the underlying bug in

# https://github.com/pandas-dev/pandas/issues/36470 is fixed.

with self.assertRaises(NotImplementedError):

self._run_test(lambda df: df.value_counts(dropna=False), df)

def test_get_column(self):

df = pd.DataFrame({

'Animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'],

'Speed': [380., 370., 24., 26.]

})

self._run_test(lambda df: df['Animal'], df)

self._run_test(lambda df: df.Speed, df)

self._run_test(lambda df: df.get('Animal'), df)

self._run_test(lambda df: df.get('FOO', df.Animal), df)

def test_series_xs(self):

# pandas doctests only verify DataFrame.xs, here we verify Series.xs as well

d = {

'num_legs': [4, 4, 2, 2],

'num_wings': [0, 0, 2, 2],

'class': ['mammal', 'mammal', 'mammal', 'bird'],

'animal': ['cat', 'dog', 'bat', 'penguin'],

'locomotion': ['walks', 'walks', 'flies', 'walks']

}

df = pd.DataFrame(data=d)

df = df.set_index(['class', 'animal', 'locomotion'])

self._run_test(lambda df: df.num_legs.xs('mammal'), df)

self._run_test(lambda df: df.num_legs.xs(('mammal', 'dog')), df)

self._run_test(lambda df: df.num_legs.xs('cat', level=1), df)

self._run_test(

lambda df: df.num_legs.xs(('bird', 'walks'), level=[0, 'locomotion']),

df)

def test_dataframe_xs(self):

# Test cases reported in BEAM-13421

df = pd.DataFrame(

np.array([

['state', 'day1', 12],

['state', 'day1', 1],

['state', 'day2', 14],

['county', 'day1', 9],

]),

columns=['provider', 'time', 'value'])

self._run_test(lambda df: df.xs('state'), df.set_index(['provider']))

self._run_test(

lambda df: df.xs('state'), df.set_index(['provider', 'time']))

def test_set_column(self):

def new_column(df):

df['NewCol'] = df['Speed']

df = pd.DataFrame({

'Animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'],

'Speed': [380., 370., 24., 26.]

})

self._run_inplace_test(new_column, df)

def test_set_column_from_index(self):

def new_column(df):

df['NewCol'] = df.index

df = pd.DataFrame({

'Animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'],

'Speed': [380., 370., 24., 26.]

})

self._run_inplace_test(new_column, df)

def test_tz_with_utc_zone_set_explicitly(self):

test = """

>>> s = pd.Series(["1/1/2020 10:00:00+00:00", "2/1/2020 11:00:00+03:00"])

>>> s = pd.to_datetime(s, utc=True)

>>> s

0 2020-01-01 10:00:00+00:00

1 2020-02-01 08:00:00+00:00

dtype: datetime64[ns, UTC]

>>> s.dt.tz

datetime.timezone.utc

"""

teststring(test)

def test_tz_localize_ambiguous_series(self):

# This replicates a tz_localize doctest:

# s.tz_localize('CET', ambiguous=np.array([True, True, False]))

# But using a DeferredSeries instead of a np array

s = pd.Series(

range(3),

index=pd.DatetimeIndex([

'2018-10-28 01:20:00', '2018-10-28 02:36:00', '2018-10-28 03:46:00'

]))

ambiguous = pd.Series([True, True, False], index=s.index)

self._run_test(

lambda s, ambiguous: s.tz_localize('CET', ambiguous=ambiguous),

s,

ambiguous)

def test_tz_convert(self):

# This replicates a tz_localize doctest:

# s.tz_localize('CET', ambiguous=np.array([True, True, False]))

# But using a DeferredSeries instead of a np array

s = pd.Series(

range(3),

index=pd.DatetimeIndex([

'2018-10-27 01:20:00', '2018-10-27 02:36:00', '2018-10-27 03:46:00'

],

tz='Europe/Berlin'))

self._run_test(lambda s: s.tz_convert('America/Los_Angeles'), s)

def test_sort_index_columns(self):

df = pd.DataFrame({

'c': range(10),

'a': range(10),

'b': range(10),

np.nan: range(10),

})

self._run_test(lambda df: df.sort_index(axis=1), df)

self._run_test(lambda df: df.sort_index(axis=1, ascending=False), df)

self._run_test(lambda df: df.sort_index(axis=1, na_position='first'), df)

def test_where_callable_args(self):

df = pd.DataFrame(

np.arange(10, dtype=np.int64).reshape(-1, 2), columns=['A', 'B'])

self._run_test(

lambda df: df.where(lambda df: df % 2 == 0, lambda df: df * 10), df)

def test_where_concrete_args(self):

df = pd.DataFrame(

np.arange(10, dtype=np.int64).reshape(-1, 2), columns=['A', 'B'])

self._run_test(

lambda df: df.where(

df % 2 == 0, pd.Series({

'A': 123, 'B': 456

}), axis=1),

df)

def test_combine_dataframe(self):

df = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})

df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})

take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2

self._run_test(

lambda df, df2: df.combine(df2, take_smaller),

df,

df2,

nonparallel=True)

def test_combine_dataframe_fill(self):

df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})

df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})

take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2

self._run_test(

lambda df1, df2: df1.combine(df2, take_smaller, fill_value=-5),

df1,

df2,

nonparallel=True)

def test_combine_Series(self):

s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0})

s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0})

self._run_test(

lambda s1, s2: s1.combine(s2, max),

s1,

s2,

nonparallel=True,

check_proxy=False)

def test_combine_first_dataframe(self):

df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})

df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})

self._run_test(lambda df1, df2: df1.combine_first(df2), df1, df2)

def test_combine_first_series(self):

s1 = pd.Series([1, np.nan])

s2 = pd.Series([3, 4])

self._run_test(lambda s1, s2: s1.combine_first(s2), s1, s2)

def test_add_prefix(self):

df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})

s = pd.Series([1, 2, 3, 4])

self._run_test(lambda df: df.add_prefix('col_'), df)

self._run_test(lambda s: s.add_prefix('col_'), s)

def test_add_suffix(self):

df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})

s = pd.Series([1, 2, 3, 4])

self._run_test(lambda df: df.add_suffix('_col'), df)

self._run_test(lambda s: s.add_prefix('_col'), s)

def test_set_index(self):

df = pd.DataFrame({

# [19, 18, ..]

'index1': reversed(range(20)), # [15, 16, .., 0, 1, .., 13, 14]

'index2': np.roll(range(20), 5), # ['', 'a', 'bb', ...]

'values': [chr(ord('a') + i) * i for i in range(20)],

})

self._run_test(lambda df: df.set_index(['index1', 'index2']), df)

self._run_test(lambda df: df.set_index(['index1', 'index2'], drop=True), df)

self._run_test(lambda df: df.set_index('values'), df)

self._run_error_test(lambda df: df.set_index('bad'), df)

self._run_error_test(

lambda df: df.set_index(['index2', 'bad', 'really_bad']), df)

def test_set_axis(self):

df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, index=['X', 'Y', 'Z'])

self._run_test(lambda df: df.set_axis(['I', 'II'], axis='columns'), df)

self._run_test(lambda df: df.set_axis([0, 1], axis=1), df)

self._run_inplace_test(

lambda df: df.set_axis(['i', 'ii'], axis='columns'), df)

with self.assertRaises(NotImplementedError):

self._run_test(lambda df: df.set_axis(['a', 'b', 'c'], axis='index'), df)

self._run_test(lambda df: df.set_axis([0, 1, 2], axis=0), df)

def test_series_set_axis(self):

s = pd.Series(list(range(3)), index=['X', 'Y', 'Z'])

with self.assertRaises(NotImplementedError):

self._run_test(lambda s: s.set_axis(['a', 'b', 'c']), s)

self._run_test(lambda s: s.set_axis([1, 2, 3]), s)

def test_series_drop_ignore_errors(self):

midx = pd.MultiIndex(

levels=[['lama', 'cow', 'falcon'], ['speed', 'weight', 'length']],

codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]])

s = pd.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3], index=midx)

# drop() requires singleton partitioning unless errors are ignored

# Add some additional tests here to make sure the implementation works in

# non-singleton partitioning.

self._run_test(lambda s: s.drop('lama', level=0, errors='ignore'), s)

self._run_test(lambda s: s.drop(('cow', 'speed'), errors='ignore'), s)

self._run_test(lambda s: s.drop('falcon', level=0, errors='ignore'), s)

def test_dataframe_drop_ignore_errors(self):

midx = pd.MultiIndex(

levels=[['lama', 'cow', 'falcon'], ['speed', 'weight', 'length']],

codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]])

df = pd.DataFrame(

index=midx,

columns=['big', 'small'],

data=[[45, 30], [200, 100], [1.5, 1], [30, 20], [250, 150], [1.5, 0.8],

[320, 250], [1, 0.8], [0.3, 0.2]])

# drop() requires singleton partitioning unless errors are ignored

# Add some additional tests here to make sure the implementation works in

# non-singleton partitioning.

self._run_test(

lambda df: df.drop(index='lama', level=0, errors='ignore'), df)

self._run_test(

lambda df: df.drop(index=('cow', 'speed'), errors='ignore'), df)

self._run_test(

lambda df: df.drop(index='falcon', level=0, errors='ignore'), df)

self._run_test(

lambda df: df.drop(index='cow', columns='small', errors='ignore'), df)

def test_merge(self):

# This is from the pandas doctests, but fails due to re-indexing being

# order-sensitive.

df1 = pd.DataFrame({

'lkey': ['foo', 'bar', 'baz', 'foo'], 'value': [1, 2, 3, 5]

})

df2 = pd.DataFrame({

'rkey': ['foo', 'bar', 'baz', 'foo'], 'value': [5, 6, 7, 8]

})

self._run_test(

lambda df1, df2: df1.merge(df2, left_on='lkey', right_on='rkey').rename(

index=lambda x: '*'),

df1,

df2,

nonparallel=True,

check_proxy=False)

self._run_test(

lambda df1, df2: df1.merge(

df2, left_on='lkey', right_on='rkey', suffixes=('_left', '_right')).

rename(index=lambda x: '*'),

df1,

df2,

nonparallel=True,

check_proxy=False)

def test_merge_left_join(self):

# This is from the pandas doctests, but fails due to re-indexing being

# order-sensitive.

df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})

df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})

self._run_test(

lambda df1, df2: df1.merge(df2, how='left', on='a').rename(

index=lambda x: '*'),

df1,

df2,

nonparallel=True,

check_proxy=False)

def test_merge_on_index(self):

# This is from the pandas doctests, but fails due to re-indexing being

# order-sensitive.

df1 = pd.DataFrame({

'lkey': ['foo', 'bar', 'baz', 'foo'], 'value': [1, 2, 3, 5]

}).set_index('lkey')

df2 = pd.DataFrame({

'rkey': ['foo', 'bar', 'baz', 'foo'], 'value': [5, 6, 7, 8]

}).set_index('rkey')

self._run_test(

lambda df1, df2: df1.merge(df2, left_index=True, right_index=True),

df1,

df2,

check_proxy=False)

def test_merge_same_key(self):

df1 = pd.DataFrame({

'key': ['foo', 'bar', 'baz', 'foo'], 'value': [1, 2, 3, 5]

})

df2 = pd.DataFrame({

'key': ['foo', 'bar', 'baz', 'foo'], 'value': [5, 6, 7, 8]

})

self._run_test(

lambda df1, df2: df1.merge(df2, on='key').rename(index=lambda x: '*'),

df1,

df2,

nonparallel=True,

check_proxy=False)

self._run_test(

lambda df1, df2: df1.merge(df2, on='key', suffixes=('_left', '_right')).

rename(index=lambda x: '*'),

df1,

df2,

nonparallel=True,

check_proxy=False)

def test_merge_same_key_doctest(self):

df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})

df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})

self._run_test(

lambda df1, df2: df1.merge(df2, how='left', on='a').rename(

index=lambda x: '*'),

df1,

df2,

nonparallel=True,

check_proxy=False)

# Test without specifying 'on'

self._run_test(

lambda df1, df2: df1.merge(df2, how='left').rename(index=lambda x: '*'),

df1,

df2,

nonparallel=True,

check_proxy=False)

def test_merge_same_key_suffix_collision(self):

df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2], 'a_lsuffix': [5, 6]})

df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4], 'a_rsuffix': [7, 8]})

self._run_test(

lambda df1, df2: df1.merge(

df2, how='left', on='a', suffixes=('_lsuffix', '_rsuffix')).rename(

index=lambda x: '*'),

df1,

df2,

nonparallel=True,

check_proxy=False)

# Test without specifying 'on'

self._run_test(

lambda df1, df2: df1.merge(

df2, how='left', suffixes=('_lsuffix', '_rsuffix')).rename(

index=lambda x: '*'),

df1,

df2,

nonparallel=True,

check_proxy=False)

def test_swaplevel(self):

df = pd.DataFrame(

{"Grade": ["A", "B", "A", "C"]},

index=[

["Final exam", "Final exam", "Coursework", "Coursework"],

["History", "Geography", "History", "Geography"],

["January", "February", "March", "April"],

])

self._run_test(lambda df: df.swaplevel(), df)

def test_value_counts_with_nans(self):

# similar to doctests that verify value_counts, but include nan values to

# make sure we handle them correctly.

df = pd.DataFrame({

'num_legs': [2, 4, 4, 6, np.nan, np.nan],

'num_wings': [2, 0, 0, 0, np.nan, 2]

},

index=['falcon', 'dog', 'cat', 'ant', 'car', 'plane'])

self._run_test(lambda df: df.value_counts(), df)

self._run_test(lambda df: df.value_counts(normalize=True), df)

# Ensure we don't drop rows due to nan values in unused columns.

self._run_test(lambda df: df.value_counts('num_wings'), df)

if PD_VERSION >= (1, 3):

# dropna=False is new in pandas 1.3

# TODO(https://github.com/apache/beam/issues/21014): Remove the

# assertRaises this when the underlying bug in

# https://github.com/pandas-dev/pandas/issues/36470 is fixed.

with self.assertRaises(NotImplementedError):

self._run_test(lambda df: df.value_counts(dropna=False), df)

# Test the defaults.

self._run_test(lambda df: df.num_wings.value_counts(), df)

self._run_test(lambda df: df.num_wings.value_counts(normalize=True), df)

self._run_test(lambda df: df.num_wings.value_counts(dropna=False), df)

# Test the combination interactions.

for normalize in (True, False):

for dropna in (True, False):

self._run_test(

lambda df, dropna=dropna, normalize=normalize: df.num_wings.

value_counts(dropna=dropna, normalize=normalize),

df)

def test_value_counts_does_not_support_sort(self):

df = pd.DataFrame({

'num_legs': [2, 4, 4, 6, np.nan, np.nan],

'num_wings': [2, 0, 0, 0, np.nan, 2]

},

index=['falcon', 'dog', 'cat', 'ant', 'car', 'plane'])

with self.assertRaisesRegex(frame_base.WontImplementError,

r"value_counts\(sort\=True\)"):

self._run_test(lambda df: df.value_counts(sort=True), df)

with self.assertRaisesRegex(frame_base.WontImplementError,

r"value_counts\(sort\=True\)"):

self._run_test(lambda df: df.num_wings.value_counts(sort=True), df)

def test_series_getitem(self):

s = pd.Series([x**2 for x in range(10)])

self._run_test(lambda s: s[...], s)

self._run_test(lambda s: s[:], s)

self._run_test(lambda s: s[s < 10], s)

self._run_test(lambda s: s[lambda s: s < 10], s)

s.index = s.index.map(float)

self._run_test(lambda s: s[1.5:6], s)

def test_series_truncate(self):

s = pd.Series(['a', 'b', 'c', 'd', 'e', 'f'])

self._run_test(lambda s: s.truncate(before=1, after=3), s)

def test_dataframe_truncate(self):

df = pd.DataFrame({

'C': list('abcde'), 'B': list('fghij'), 'A': list('klmno')

},

index=[1, 2, 3, 4, 5])

self._run_test(lambda df: df.truncate(before=1, after=3), df)

self._run_test(lambda df: df.truncate(before='A', after='B', axis=1), df)

self._run_test(lambda df: df['A'].truncate(before=2, after=4), df)

@parameterized.expand([

(pd.Series(range(10)), ), # unique

(pd.Series(list(range(100)) + [0]), ), # non-unique int

(pd.Series(list(range(100)) + [0]) / 100, ), # non-unique flt

(pd.Series(['a', 'b', 'c', 'd']), ), # unique str

(pd.Series(['a', 'b', 'a', 'c', 'd']), ), # non-unique str

])

def test_series_is_unique(self, series):

self._run_test(lambda s: s.is_unique, series)

@parameterized.expand([

(pd.Series(range(10)), ), # False

(pd.Series([1, 2, np.nan, 3, np.nan]), ), # True

(pd.Series(['a', 'b', 'c', 'd', 'e']), ), # False

(pd.Series(['a', 'b', None, 'c', None]), ), # True

])

def test_series_hasnans(self, series):

self._run_test(lambda s: s.hasnans, series)

def test_dataframe_getitem(self):

df = pd.DataFrame({'A': [x**2 for x in range(6)], 'B': list('abcdef')})

self._run_test(lambda df: df['A'], df)

self._run_test(lambda df: df[['A', 'B']], df)

self._run_test(lambda df: df[:], df)

self._run_test(lambda df: df[df.A < 10], df)

df.index = df.index.map(float)

self._run_test(lambda df: df[1.5:4], df)

def test_loc(self):

dates = pd.date_range('1/1/2000', periods=8)

# TODO(https://github.com/apache/beam/issues/20765):

# We do not preserve the freq attribute on a DateTime index

dates.freq = None

df = pd.DataFrame(

np.arange(32).reshape((8, 4)),

index=dates,

columns=['A', 'B', 'C', 'D'])

self._run_test(lambda df: df.loc[:], df)

self._run_test(lambda df: df.loc[:, 'A'], df)

self._run_test(lambda df: df.loc[:dates[3]], df)

self._run_test(lambda df: df.loc[df.A > 10], df)

self._run_test(lambda df: df.loc[lambda df: df.A > 10], df)

self._run_test(lambda df: df.C.loc[df.A > 10], df)

self._run_test(lambda df, s: df.loc[s.loc[1:3]], df, pd.Series(dates))

@unittest.skipIf(PD_VERSION >= (2, 0), 'append removed in Pandas 2.0')

def test_append_sort(self):

# yapf: disable

df1 = pd.DataFrame({'int': [1, 2, 3], 'str': ['a', 'b', 'c']},

columns=['int', 'str'],

index=[1, 3, 5])

df2 = pd.DataFrame({'int': [4, 5, 6], 'str': ['d', 'e', 'f']},

columns=['str', 'int'],

index=[2, 4, 6])

# yapf: enable

self._run_test(lambda df1, df2: df1.append(df2, sort=True), df1, df2)

self._run_test(lambda df1, df2: df1.append(df2, sort=False), df1, df2)

self._run_test(lambda df1, df2: df2.append(df1, sort=True), df1, df2)

self._run_test(lambda df1, df2: df2.append(df1, sort=False), df1, df2)

def test_smallest_largest(self):

df = pd.DataFrame({'A': [1, 1, 2, 2], 'B': [2, 3, 5, 7]})

self._run_test(lambda df: df.nlargest(1, 'A', keep='all'), df)

self._run_test(lambda df: df.nsmallest(3, 'A', keep='all'), df)

self._run_test(lambda df: df.nlargest(3, ['A', 'B'], keep='all'), df)

def test_series_cov_corr(self):

for s in [pd.Series([1, 2, 3]),

pd.Series(range(100)),

pd.Series([x**3 for x in range(-50, 50)])]:

self._run_test(lambda s: s.std(), s)

self._run_test(lambda s: s.var(), s)

self._run_test(lambda s: s.corr(s), s)

self._run_test(lambda s: s.corr(s + 1), s)

self._run_test(lambda s: s.corr(s * s), s)

self._run_test(lambda s: s.cov(s * s), s)

self._run_test(lambda s: s.skew(), s)

self._run_test(lambda s: s.kurtosis(), s)

self._run_test(lambda s: s.kurt(), s)

def test_dataframe_cov_corr(self):

df = pd.DataFrame(np.random.randn(20, 3), columns=['a', 'b', 'c'])

df.loc[df.index[:5], 'a'] = np.nan

df.loc[df.index[5:10], 'b'] = np.nan

self._run_test(lambda df: df.corr(), df)

self._run_test(lambda df: df.cov(), df)

self._run_test(lambda df: df.corr(min_periods=12), df)

self._run_test(lambda df: df.cov(min_periods=12), df)

self._run_test(lambda df: df.corrwith(df.a), df)

self._run_test(lambda df: df[['a', 'b']].corrwith(df[['b', 'c']]), df)

df2 = pd.DataFrame(np.random.randn(20, 3), columns=['a', 'b', 'c'])

self._run_test(

lambda df, df2: df.corrwith(df2, axis=1), df, df2, check_proxy=False)

def test_corrwith_bad_axis(self):

df = pd.DataFrame({'a': range(3), 'b': range(3, 6), 'c': range(6, 9)})

self._run_error_test(lambda df: df.corrwith(df.a, axis=2), df)

self._run_error_test(lambda df: df.corrwith(df, axis=5), df)

@unittest.skipIf(PD_VERSION < (1, 2), "na_action added in pandas 1.2.0")

@pytest.mark.filterwarnings(

"ignore:The default of observed=False is deprecated:FutureWarning")

def test_applymap_na_action(self):

# Replicates a doctest for na_action which is incompatible with

# doctest framework

df = pd.DataFrame([[pd.NA, 2.12], [3.356, 4.567]])

self._run_test(

lambda df: df.applymap(lambda x: len(str(x)), na_action='ignore'),

df,

# TODO: generate proxy using naive type inference on fn

check_proxy=False)

@unittest.skipIf(PD_VERSION < (2, 1), "map added in 2.1.0")

def test_map_na_action(self):

# Replicates a doctest for na_action which is incompatible with

# doctest framework

df = pd.DataFrame([[pd.NA, 2.12], [3.356, 4.567]])

self._run_test(

lambda df: df.map(lambda x: len(str(x)), na_action='ignore'),

df,

# TODO: generate proxy using naive type inference on fn

check_proxy=False)

def test_dataframe_eval_query(self):

df = pd.DataFrame(np.random.randn(20, 3), columns=['a', 'b', 'c'])

self._run_test(lambda df: df.eval('foo = a + b - c'), df)

self._run_test(lambda df: df.query('a > b + c'), df)

self._run_inplace_test(lambda df: df.eval('foo = a + b - c'), df)

# Verify that attempting to access locals raises a useful error

deferred_df = frame_base.DeferredFrame.wrap(

expressions.ConstantExpression(df, df[0:0]))

self.assertRaises(

NotImplementedError, lambda: deferred_df.eval('foo = a + @b - c'))

self.assertRaises(

NotImplementedError, lambda: deferred_df.query('a > @b + c'))

def test_index_name_assignment(self):

df = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})

df = df.set_index(['a', 'b'], drop=False)

def change_index_names(df):

df.index.names = ['A', None]

self._run_inplace_test(change_index_names, df)

def test_quantile(self):

df = pd.DataFrame(

np.array([[1, 1], [2, 10], [3, 100], [4, 100]]), columns=['a', 'b'])

self._run_test(

lambda df: df.quantile(0.1, axis='columns'), df, check_proxy=False)

self._run_test(

lambda df: df.quantile(0.1, axis='columns'), df, check_proxy=False)

with self.assertRaisesRegex(frame_base.WontImplementError,

r"df\.quantile\(q=0\.1, axis='columns'\)"):

self._run_test(lambda df: df.quantile([0.1, 0.5], axis='columns'), df)

def test_dataframe_melt(self):

df = pd.DataFrame({

'A': {

0: 'a', 1: 'b', 2: 'c'

},

'B': {

0: 1, 1: 3, 2: 5

},

'C': {

0: 2, 1: 4, 2: 6

}

})

self._run_test(

lambda df: df.melt(id_vars=['A'], value_vars=['B'], ignore_index=False),

df)

self._run_test(

lambda df: df.melt(

id_vars=['A'], value_vars=['B', 'C'], ignore_index=False),

df)

self._run_test(

lambda df: df.melt(

id_vars=['A'], value_vars=['B'], var_name='myVarname', value_name=

'myValname', ignore_index=False),

df)

self._run_test(

lambda df: df.melt(

id_vars=['A'], value_vars=['B', 'C'], ignore_index=False),

df)

df.columns = [list('ABC'), list('DEF')]

self._run_test(

lambda df: df.melt(

col_level=0, id_vars=['A'], value_vars=['B'], ignore_index=False),

df)

self._run_test(

lambda df: df.melt(

id_vars=[('A', 'D')], value_vars=[('B', 'E')], ignore_index=False),

df)

def test_fillna_columns(self):

df = pd.DataFrame(

[[np.nan, 2, np.nan, 0], [3, 4, np.nan, 1], [np.nan, np.nan, np.nan, 5],

[np.nan, 3, np.nan, 4], [3, np.nan, np.nan, 4]],

columns=list('ABCD'))

self._run_test(lambda df: df.fillna(method='ffill', axis='columns'), df)

self._run_test(

lambda df: df.fillna(method='ffill', axis='columns', limit=1), df)

self._run_test(

lambda df: df.fillna(method='bfill', axis='columns', limit=1), df)

# Intended behavior is unclear here. See

# https://github.com/pandas-dev/pandas/issues/40989

# self._run_test(lambda df: df.fillna(axis='columns', value=100,

# limit=2), df)

def test_dataframe_fillna_dataframe_as_value(self):

df = pd.DataFrame([[np.nan, 2, np.nan, 0], [3, 4, np.nan, 1],

[np.nan, np.nan, np.nan, 5], [np.nan, 3, np.nan, 4]],

columns=list("ABCD"))

df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))

self._run_test(lambda df, df2: df.fillna(df2), df, df2)

def test_dataframe_fillna_series_as_value(self):

df = pd.DataFrame([[np.nan, 2, np.nan, 0], [3, 4, np.nan, 1],

[np.nan, np.nan, np.nan, 5], [np.nan, 3, np.nan, 4]],

columns=list("ABCD"))

s = pd.Series(range(4), index=list("ABCE"))