Name	Size	Mode	Actions
aggregate/	-	0755	rm
transform/	-	0755	rm
__pycache__/	-	0755	rm
conftest.py	3425	0644	editdlrm
test_allowlist.py	10410	0644	editdlrm
test_apply.py	31700	0644	editdlrm
test_apply_mutate.py	1849	0644	editdlrm
test_bin_groupby.py	4083	0644	editdlrm
test_categorical.py	56137	0644	editdlrm
test_counting.py	12289	0644	editdlrm
test_filters.py	20402	0644	editdlrm
test_function.py	32913	0644	editdlrm
test_groupby.py	62594	0644	editdlrm
test_groupby_dropna.py	7193	0644	editdlrm
test_groupby_subclass.py	2676	0644	editdlrm
test_grouping.py	35091	0644	editdlrm
test_index_as_string.py	2069	0644	editdlrm
test_nth.py	21112	0644	editdlrm
test_nunique.py	5803	0644	editdlrm
test_pipe.py	2075	0644	editdlrm
test_quantile.py	8236	0644	editdlrm
test_rank.py	15447	0644	editdlrm
test_sample.py	4358	0644	editdlrm
test_size.py	2149	0644	editdlrm
test_timegrouper.py	28453	0644	editdlrm
test_value_counts.py	3470	0644	editdlrm
__init__.py	0	0644	editdlrm

import numpy as np import pytest from pandas import DataFrame, MultiIndex import pandas._testing as tm from pandas.core.groupby.base import reduction_kernels, transformation_kernels @pytest.fixture def mframe(): index = MultiIndex( levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]], codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], names=["first", "second"], ) return DataFrame(np.random.randn(10, 3), index=index, columns=["A", "B", "C"]) @pytest.fixture def df(): return DataFrame( { "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"], "B": ["one", "one", "two", "three", "two", "two", "one", "three"], "C": np.random.randn(8), "D": np.random.randn(8), } ) @pytest.fixture def ts(): return tm.makeTimeSeries() @pytest.fixture def tsd(): return tm.getTimeSeriesData() @pytest.fixture def tsframe(tsd): return DataFrame(tsd) @pytest.fixture def df_mixed_floats(): return DataFrame( { "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"], "B": ["one", "one", "two", "three", "two", "two", "one", "three"], "C": np.random.randn(8), "D": np.array(np.random.randn(8), dtype="float32"), } ) @pytest.fixture def three_group(): return DataFrame( { "A": [ "foo", "foo", "foo", "foo", "bar", "bar", "bar", "bar", "foo", "foo", "foo", ], "B": [ "one", "one", "one", "two", "one", "one", "one", "two", "two", "two", "one", ], "C": [ "dull", "dull", "shiny", "dull", "dull", "shiny", "shiny", "dull", "shiny", "shiny", "shiny", ], "D": np.random.randn(11), "E": np.random.randn(11), "F": np.random.randn(11), } ) @pytest.fixture(params=sorted(reduction_kernels)) def reduction_func(request): """ yields the string names of all groupby reduction functions, one at a time. """ return request.param @pytest.fixture(params=sorted(transformation_kernels)) def transformation_func(request): """yields the string names of all groupby transformation functions.""" return request.param @pytest.fixture(params=sorted(reduction_kernels) + sorted(transformation_kernels)) def groupby_func(request): """yields both aggregation and transformation functions.""" return request.param @pytest.fixture(params=[True, False]) def parallel(request): """parallel keyword argument for numba.jit""" return request.param @pytest.fixture(params=[True, False]) def nogil(request): """nogil keyword argument for numba.jit""" return request.param @pytest.fixture(params=[True, False]) def nopython(request): """nopython keyword argument for numba.jit""" return request.param