from __future__ import annotations import contextlib import operator import sys import warnings from datetime import datetime from functools import partial import numpy as np import pandas as pd import pytest import dask import dask.dataframe as dd from dask._compatibility import WINDOWS from dask.dataframe import _compat from dask.dataframe._compat import ( PANDAS_GE_210, PANDAS_GE_220, PANDAS_GE_230, PANDAS_GE_300, check_observed_deprecation, tm, ) from dask.dataframe._pyarrow import to_pyarrow_string from dask.dataframe.backends import grouper_dispatch from dask.dataframe.groupby import NUMERIC_ONLY_NOT_IMPLEMENTED from dask.dataframe.utils import assert_dask_graph, assert_eq, pyarrow_strings_enabled from dask.utils import M from dask.utils_test import hlg_layer DASK_EXPR_ENABLED = dd._dask_expr_enabled() AGG_FUNCS = [ "sum", pytest.param( "mean", marks=pytest.mark.xfail( reason="numeric_only=False not implemented", strict=False, ), ), "median", "min", "max", "count", "size", pytest.param( "std", marks=pytest.mark.xfail( reason="numeric_only=False not implemented", strict=False, ), ), pytest.param( "var", marks=pytest.mark.xfail( reason="numeric_only=False not implemented", strict=False, ), ), pytest.param( "cov", marks=pytest.mark.xfail( reason="numeric_only=False not implemented", strict=False, ), ), pytest.param( "corr", marks=pytest.mark.xfail( reason="numeric_only=False not implemented", strict=False, ), ), "nunique", "first", "last", "prod", ] INCLUDE_GROUPS = {"include_groups": False} if PANDAS_GE_220 else {} @pytest.fixture(params=AGG_FUNCS) def agg_func(request): """ Aggregations supported for groups """ return request.param # Wrapper fixture for shuffle_method to auto-apply it to all the tests in this module, # as we don't want to auto-apply the fixture repo-wide. @pytest.fixture(autouse=True) def auto_shuffle_method(shuffle_method): yield @contextlib.contextmanager def groupby_axis_deprecated(*contexts, dask_op=True): with contextlib.ExitStack() as stack: for ctx in contexts: stack.enter_context(ctx) if PANDAS_GE_210 and (not DASK_EXPR_ENABLED or not dask_op): stack.enter_context(pytest.warns(FutureWarning, match="axis")) yield @pytest.mark.xfail(reason="uncertain how to handle. See issue #3481.") def test_groupby_internal_repr_xfail(): pdf = pd.DataFrame({"x": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10], "y": list("abcbabbcda")}) ddf = dd.from_pandas(pdf, 3) gp = pdf.groupby("y")["x"] dp = ddf.groupby("y")["x"] assert isinstance(dp.obj, dd.Series) assert_eq(dp.obj, gp.obj) gp = pdf.groupby(pdf.y)["x"] dp = ddf.groupby(ddf.y)["x"] assert isinstance(dp.obj, dd.Series) @pytest.mark.skipif(DASK_EXPR_ENABLED, reason="architecture different") def test_groupby_internal_repr(): pdf = pd.DataFrame({"x": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10], "y": list("abcbabbcda")}) ddf = dd.from_pandas(pdf, 3) gp = pdf.groupby("y") dp = ddf.groupby("y") assert isinstance(dp, dd.groupby.DataFrameGroupBy) assert isinstance(dp._meta, pd.core.groupby.DataFrameGroupBy) assert isinstance(dp.obj, dd.DataFrame) assert_eq(dp.obj, gp.obj) gp = pdf.groupby("y")["x"] dp = ddf.groupby("y")["x"] assert isinstance(dp, dd.groupby.SeriesGroupBy) assert isinstance(dp._meta, pd.core.groupby.SeriesGroupBy) gp = pdf.groupby("y")[["x"]] dp = ddf.groupby("y")[["x"]] assert isinstance(dp, dd.groupby.DataFrameGroupBy) assert isinstance(dp._meta, pd.core.groupby.DataFrameGroupBy) # slicing should not affect to internal assert isinstance(dp.obj, dd.DataFrame) assert_eq(dp.obj, gp.obj) gp = pdf.groupby(pdf.y)["x"] dp = ddf.groupby(ddf.y)["x"] assert isinstance(dp, dd.groupby.SeriesGroupBy) assert isinstance(dp._meta, pd.core.groupby.SeriesGroupBy) gp = pdf.groupby(pdf.y)[["x"]] dp = ddf.groupby(ddf.y)[["x"]] assert isinstance(dp, dd.groupby.DataFrameGroupBy) assert isinstance(dp._meta, pd.core.groupby.DataFrameGroupBy) # slicing should not affect to internal assert isinstance(dp.obj, dd.DataFrame) assert_eq(dp.obj, gp.obj) def test_groupby_error(): pdf = pd.DataFrame({"x": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10], "y": list("abcbabbcda")}) ddf = dd.from_pandas(pdf, 3) with pytest.raises(KeyError): ddf.groupby("A") with pytest.raises(KeyError): ddf.groupby(["x", "A"]) dp = ddf.groupby("y") msg = "Column not found: " with pytest.raises(KeyError) as err: dp["A"] assert msg in str(err.value) msg = "Columns not found: " with pytest.raises(KeyError) as err: dp[["x", "A"]] assert msg in str(err.value) msg = ( "DataFrameGroupBy does not allow compute method." "Please chain it with an aggregation method (like ``.mean()``) or get a " "specific group using ``.get_group()`` before calling ``compute()``" ) with pytest.raises(NotImplementedError) as err: dp.compute() assert msg in str(err.value) def test_full_groupby(): df = pd.DataFrame( {"a": [1, 2, 3, 4, 5, 6, 7, 8, 9], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0]}, index=[0, 1, 3, 5, 6, 8, 9, 9, 9], ) ddf = dd.from_pandas(df, npartitions=3) pytest.raises(KeyError, lambda: ddf.groupby("does_not_exist")) pytest.raises(AttributeError, lambda: ddf.groupby("a").does_not_exist) assert "b" in dir(ddf.groupby("a")) def func(df): return df.assign(b=df.b - df.b.mean()) expected = df.groupby("a").apply(func, **INCLUDE_GROUPS) with pytest.warns(UserWarning, match="`meta` is not specified"): if not DASK_EXPR_ENABLED: assert ( ddf.groupby("a").apply(func, **INCLUDE_GROUPS)._name.startswith("func") ) assert_eq(expected, ddf.groupby("a").apply(func, **INCLUDE_GROUPS)) @pytest.mark.xfail( DASK_EXPR_ENABLED, reason="can't support collections in kwargs for apply" ) def test_full_groupby_apply_multiarg(): df = pd.DataFrame( {"a": [1, 2, 3, 4, 5, 6, 7, 8, 9], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0]}, index=[0, 1, 3, 5, 6, 8, 9, 9, 9], ) ddf = dd.from_pandas(df, npartitions=3) def func(df, c, d=3): return df.assign(b=df.b - df.b.mean() + c * d) c = df.a.sum() d = df.b.mean() c_scalar = ddf.a.sum() d_scalar = ddf.b.mean() c_delayed = dask.delayed(lambda: c)() d_delayed = dask.delayed(lambda: d)() with pytest.warns(UserWarning, match="`meta` is not specified"): assert_eq( df.groupby("a").apply(func, c, d=d, **INCLUDE_GROUPS), ddf.groupby("a").apply(func, c, d=d_scalar, **INCLUDE_GROUPS), ) assert_eq( df.groupby("a").apply(func, c, **INCLUDE_GROUPS), ddf.groupby("a").apply(func, c, **INCLUDE_GROUPS), ) assert_eq( df.groupby("a").apply(func, c, d=d, **INCLUDE_GROUPS), ddf.groupby("a").apply(func, c, d=d, **INCLUDE_GROUPS), ) assert_eq( df.groupby("a").apply(func, c, **INCLUDE_GROUPS), ddf.groupby("a").apply(func, c_scalar, **INCLUDE_GROUPS), check_dtype=False, ) meta = df.groupby("a").apply(func, c, **INCLUDE_GROUPS) assert_eq( df.groupby("a").apply(func, c, **INCLUDE_GROUPS), ddf.groupby("a").apply(func, c_scalar, meta=meta, **INCLUDE_GROUPS), ) assert_eq( df.groupby("a").apply(func, c, d=d, **INCLUDE_GROUPS), ddf.groupby("a").apply(func, c, d=d_scalar, meta=meta, **INCLUDE_GROUPS), ) # Delayed arguments work, but only if metadata is provided with pytest.raises(ValueError) as exc: ddf.groupby("a").apply(func, c, d=d_delayed, **INCLUDE_GROUPS) assert "dask.delayed" in str(exc.value) and "meta" in str(exc.value) with pytest.raises(ValueError) as exc: ddf.groupby("a").apply(func, c_delayed, d=d, **INCLUDE_GROUPS) assert "dask.delayed" in str(exc.value) and "meta" in str(exc.value) assert_eq( df.groupby("a").apply(func, c, **INCLUDE_GROUPS), ddf.groupby("a").apply(func, c_delayed, meta=meta, **INCLUDE_GROUPS), ) assert_eq( df.groupby("a").apply(func, c, d=d, **INCLUDE_GROUPS), ddf.groupby("a").apply(func, c, d=d_delayed, meta=meta, **INCLUDE_GROUPS), ) @pytest.mark.parametrize( "grouper", [ lambda df: ["a"], lambda df: ["a", "b"], lambda df: df["a"], lambda df: [df["a"], df["b"]], pytest.param( lambda df: [df["a"] > 2, df["b"] > 1], marks=pytest.mark.xfail(not DASK_EXPR_ENABLED, reason="not yet supported"), ), ], ) @pytest.mark.parametrize("reverse", [True, False]) def test_full_groupby_multilevel(grouper, reverse): index = [0, 1, 3, 5, 6, 8, 9, 9, 9] if reverse: index = index[::-1] df = pd.DataFrame( { "a": [1, 2, 3, 4, 5, 6, 7, 8, 9], "d": [1, 2, 3, 4, 5, 6, 7, 8, 9], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0], }, index=index, ) ddf = dd.from_pandas(df, npartitions=3) def func(df): return df.assign(b=df.d - df.d.mean()) with pytest.warns(UserWarning, match="`meta` is not specified"): assert_eq( df.groupby(grouper(df)).apply(func, **INCLUDE_GROUPS), ddf.groupby(grouper(ddf)).apply(func, **INCLUDE_GROUPS), ) def test_groupby_dir(): df = pd.DataFrame({"a": range(10), "b c d e": range(10)}) ddf = dd.from_pandas(df, npartitions=2) g = ddf.groupby("a") assert "a" in dir(g) assert "b c d e" not in dir(g) @pytest.mark.parametrize("scheduler", ["sync", "threads"]) def test_groupby_on_index(scheduler): pdf = pd.DataFrame( {"a": [1, 2, 3, 4, 5, 6, 7, 8, 9], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0]}, index=[0, 1, 3, 5, 6, 8, 9, 9, 9], ) ddf = dd.from_pandas(pdf, npartitions=3) ddf2 = ddf.set_index("a") pdf2 = pdf.set_index("a") assert_eq(ddf.groupby("a").b.mean(), ddf2.groupby(ddf2.index).b.mean()) # Check column projection for `groupby().agg` agg = ddf2.groupby("a").agg({"b": "mean"}) assert_eq(ddf.groupby("a").b.mean(), agg.b) if not DASK_EXPR_ENABLED: assert hlg_layer(agg.dask, "getitem") def func(df): return df.assign(b=df.b - df.b.mean()) def func2(df): return df[["b"]] - df[["b"]].mean() def func3(df): return df.mean() with dask.config.set(scheduler=scheduler): with warnings.catch_warnings(): warnings.simplefilter("ignore", UserWarning) assert_eq( ddf.groupby("a").apply(func, **INCLUDE_GROUPS), pdf.groupby("a").apply(func, **INCLUDE_GROUPS), ) assert_eq( pdf2.groupby(pdf2.index).apply(func2, **INCLUDE_GROUPS), ddf2.groupby(ddf2.index).apply(func2, **INCLUDE_GROUPS), ) assert_eq( ddf2.b.groupby("a").apply(func3, **INCLUDE_GROUPS), pdf2.b.groupby("a").apply(func3, **INCLUDE_GROUPS), ) assert_eq( ddf2.b.groupby(ddf2.index).apply(func3, **INCLUDE_GROUPS), pdf2.b.groupby(pdf2.index).apply(func3, **INCLUDE_GROUPS), ) @pytest.mark.parametrize( "grouper", [ lambda df: df.groupby("a")["b"], lambda df: df.groupby(["a", "b"]), lambda df: df.groupby(["a", "b"])["c"], lambda df: df.groupby(df["a"])[["b", "c"]], lambda df: df.groupby("a")[["b", "c"]], lambda df: df.groupby("a")[["b"]], lambda df: df.groupby(["a", "b", "c"]), ], ) def test_groupby_multilevel_getitem(grouper, agg_func): # nunique is not implemented for DataFrameGroupBy if agg_func == "nunique": return df = pd.DataFrame( { "a": [1, 2, 3, 1, 2, 3], "b": [1, 2, 1, 4, 2, 1], "c": [1, 3, 2, 1, 1, 2], "d": [1, 2, 1, 1, 2, 2], } ) ddf = dd.from_pandas(df, 2) dask_group = grouper(ddf) pandas_group = grouper(df) # covariance/correlation only works with N+1 columns if isinstance(pandas_group, pd.core.groupby.SeriesGroupBy) and agg_func in ( "cov", "corr", ): return dask_agg = getattr(dask_group, agg_func) pandas_agg = getattr(pandas_group, agg_func) if not DASK_EXPR_ENABLED: assert isinstance(dask_group, dd.groupby._GroupBy) assert isinstance(pandas_group, pd.core.groupby.GroupBy) if agg_func == "mean": assert_eq(dask_agg(), pandas_agg().astype(float)) else: a = dask_agg() with warnings.catch_warnings(): # pandas does `.cov([[1], [1]])` which numpy warns on (all NaN). # Pandas does strange things with exceptions in groupby. warnings.simplefilter("ignore", RuntimeWarning) b = pandas_agg() assert_eq(a, b) def test_groupby_multilevel_agg(): df = pd.DataFrame( { "a": [1, 2, 3, 1, 2, 3], "b": [1, 2, 1, 4, 2, 1], "c": [1, 3, 2, 1, 1, 2], "d": [1, 2, 1, 1, 2, 2], } ) ddf = dd.from_pandas(df, 2) sol = df.groupby(["a"]).mean() res = ddf.groupby(["a"]).mean() assert_eq(res, sol) sol = df.groupby(["a", "c"]).mean() res = ddf.groupby(["a", "c"]).mean() assert_eq(res, sol) sol = df.groupby([df["a"], df["c"]]).mean() res = ddf.groupby([ddf["a"], ddf["c"]]).mean() assert_eq(res, sol) @pytest.mark.parametrize( "categoricals,by", [ (True, lambda df: "b"), (False, lambda df: "b"), (True, lambda df: df.b), (False, lambda df: df.b), (False, lambda df: df.b + 1), ], ) def test_groupby_get_group(categoricals, by): dsk = { ("x", 0): pd.DataFrame({"a": [1, 2, 6], "b": [4, 2, 7]}, index=[0, 1, 3]), ("x", 1): pd.DataFrame({"a": [4, 2, 6], "b": [3, 3, 1]}, index=[5, 6, 8]), ("x", 2): pd.DataFrame({"a": [4, 3, 7], "b": [1, 1, 3]}, index=[9, 9, 9]), } if not DASK_EXPR_ENABLED: meta = dsk[("x", 0)] ddf = dd.DataFrame(dsk, "x", meta, [0, 4, 9, 9]) else: ddf = dd.repartition(pd.concat(dsk.values()), divisions=[0, 4, 9, 9]) if categoricals: ddf = ddf.categorize(columns=["b"]) pdf = ddf.compute() if PANDAS_GE_210 and categoricals and not PANDAS_GE_300: ctx = pytest.warns(FutureWarning, match="The default of observed=False") else: ctx = contextlib.nullcontext() with ctx: ddgrouped = ddf.groupby(by(ddf)) with ctx: pdgrouped = pdf.groupby(by(pdf)) # DataFrame assert_eq(ddgrouped.get_group(2), pdgrouped.get_group(2)) assert_eq(ddgrouped.get_group(3), pdgrouped.get_group(3)) # Series assert_eq(ddgrouped.a.get_group(3), pdgrouped.a.get_group(3)) assert_eq(ddgrouped.a.get_group(2), pdgrouped.a.get_group(2)) def test_dataframe_groupby_nunique(): strings = list("aaabbccccdddeee") data = np.random.randn(len(strings)) ps = pd.DataFrame(dict(strings=strings, data=data)) s = dd.from_pandas(ps, npartitions=3) expected = ps.groupby("strings")["data"].nunique() result = s.groupby("strings")["data"].nunique() assert_eq(result, expected) def test_dataframe_groupby_nunique_across_group_same_value(): strings = list("aaabbccccdddeee") data = list(map(int, "123111223323412")) ps = pd.DataFrame(dict(strings=strings, data=data)) s = dd.from_pandas(ps, npartitions=3) expected = ps.groupby("strings")["data"].nunique() result = s.groupby("strings")["data"].nunique() assert_eq(result, expected) def test_series_groupby_propagates_names(): df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]}) ddf = dd.from_pandas(df, 2) func = lambda df: df["y"].sum() with pytest.warns(UserWarning): # meta inference result = ddf.groupby("x").apply(func, **INCLUDE_GROUPS) expected = df.groupby("x").apply(func, **INCLUDE_GROUPS) assert_eq(result, expected) @pytest.mark.parametrize("npartitions", (1, 2)) @pytest.mark.parametrize("func", ("cumsum", "cumprod", "cumcount")) def test_series_groupby_cumfunc_with_named_index(npartitions, func): df = pd.DataFrame( {"x": [1, 2, 3, 4, 5, 6, 7], "y": [8, 9, 6, 2, 3, 5, 6]} ).set_index("x") ddf = dd.from_pandas(df, npartitions) assert ddf.npartitions == npartitions expected = getattr(df["y"].groupby("x"), func)() result = getattr(ddf["y"].groupby("x"), func)() assert_eq(result, expected) def test_series_groupby(): s = pd.Series([1, 2, 2, 1, 1]) pd_group = s.groupby(s) ss = dd.from_pandas(s, npartitions=2) dask_group = ss.groupby(ss) pd_group2 = s.groupby(s + 1) dask_group2 = ss.groupby(ss + 1) for dg, pdg in [(dask_group, pd_group), (pd_group2, dask_group2)]: assert_eq(dg.count(), pdg.count()) assert_eq(dg.sum(), pdg.sum()) assert_eq(dg.min(), pdg.min()) assert_eq(dg.max(), pdg.max()) assert_eq(dg.size(), pdg.size()) assert_eq(dg.first(), pdg.first()) assert_eq(dg.last(), pdg.last()) assert_eq(dg.prod(), pdg.prod()) def test_series_groupby_errors(): s = pd.Series([1, 2, 2, 1, 1]) ss = dd.from_pandas(s, npartitions=2) msg = "No group keys passed!" with pytest.raises(ValueError) as err: s.groupby([]) # pandas assert msg in str(err.value) with pytest.raises(ValueError) as err: ss.groupby([]) # dask should raise the same error assert msg in str(err.value) sss = dd.from_pandas(s, npartitions=5) with pytest.raises((NotImplementedError, ValueError)): ss.groupby(sss) with pytest.raises(KeyError): s.groupby("x") # pandas with pytest.raises(KeyError): ss.groupby("x") # dask should raise the same error def test_groupby_index_array(): df = _compat.makeTimeDataFrame() ddf = dd.from_pandas(df, npartitions=2) # first select column, then group assert_eq( df.A.groupby(df.index.month).nunique(), ddf.A.groupby(ddf.index.month).nunique(), check_names=False, ) # first group, then select column assert_eq( df.groupby(df.index.month).A.nunique(), ddf.groupby(ddf.index.month).A.nunique(), check_names=False, ) def test_groupby_set_index(): df = _compat.makeTimeDataFrame() ddf = dd.from_pandas(df, npartitions=2) pytest.raises(TypeError, lambda: ddf.groupby(df.index.month, as_index=False)) @pytest.mark.parametrize("empty", [True, False]) def test_split_apply_combine_on_series(empty): if empty: pdf = pd.DataFrame({"a": [1.0], "b": [1.0]}, index=[0]).iloc[:0] # There's a bug in pandas where df.groupby(...).var(ddof=0) results in # no columns. Just skip these checks for now. ddofs = [] else: ddofs = [0, 1, 2] pdf = pd.DataFrame( {"a": [1, 2, 6, 4, 4, 6, 4, 3, 7], "b": [4, 2, 7, 3, 3, 1, 1, 1, 2]}, index=[0, 1, 3, 5, 6, 8, 9, 9, 9], ) ddf = dd.from_pandas(pdf, npartitions=3) for ddkey, pdkey in [("b", "b"), (ddf.b, pdf.b), (ddf.b + 1, pdf.b + 1)]: assert_eq(ddf.groupby(ddkey).a.min(), pdf.groupby(pdkey).a.min()) assert_eq(ddf.groupby(ddkey).a.max(), pdf.groupby(pdkey).a.max()) assert_eq(ddf.groupby(ddkey).a.count(), pdf.groupby(pdkey).a.count()) assert_eq(ddf.groupby(ddkey).a.mean(), pdf.groupby(pdkey).a.mean()) assert_eq(ddf.groupby(ddkey).a.nunique(), pdf.groupby(pdkey).a.nunique()) assert_eq(ddf.groupby(ddkey).a.size(), pdf.groupby(pdkey).a.size()) assert_eq(ddf.groupby(ddkey).a.first(), pdf.groupby(pdkey).a.first()) assert_eq(ddf.groupby(ddkey).a.last(), pdf.groupby(pdkey).a.last()) assert_eq(ddf.groupby(ddkey).a.tail(), pdf.groupby(pdkey).a.tail()) assert_eq(ddf.groupby(ddkey).a.head(), pdf.groupby(pdkey).a.head()) for ddof in ddofs: assert_eq(ddf.groupby(ddkey).a.var(ddof), pdf.groupby(pdkey).a.var(ddof)) assert_eq(ddf.groupby(ddkey).a.std(ddof), pdf.groupby(pdkey).a.std(ddof)) assert_eq(ddf.groupby(ddkey).sum(), pdf.groupby(pdkey).sum()) assert_eq(ddf.groupby(ddkey).min(), pdf.groupby(pdkey).min()) assert_eq(ddf.groupby(ddkey).max(), pdf.groupby(pdkey).max()) assert_eq(ddf.groupby(ddkey).count(), pdf.groupby(pdkey).count()) assert_eq(ddf.groupby(ddkey).mean(), pdf.groupby(pdkey).mean()) assert_eq(ddf.groupby(ddkey).size(), pdf.groupby(pdkey).size()) assert_eq(ddf.groupby(ddkey).first(), pdf.groupby(pdkey).first()) assert_eq(ddf.groupby(ddkey).last(), pdf.groupby(pdkey).last()) assert_eq(ddf.groupby(ddkey).prod(), pdf.groupby(pdkey).prod()) for ddof in ddofs: assert_eq( ddf.groupby(ddkey).var(ddof), pdf.groupby(pdkey).var(ddof), check_dtype=False, ) assert_eq( ddf.groupby(ddkey).std(ddof), pdf.groupby(pdkey).std(ddof), check_dtype=False, ) for ddkey, pdkey in [(ddf.b, pdf.b), (ddf.b + 1, pdf.b + 1)]: assert_eq( ddf.a.groupby(ddkey).sum(), pdf.a.groupby(pdkey).sum(), check_names=False ) assert_eq( ddf.a.groupby(ddkey).max(), pdf.a.groupby(pdkey).max(), check_names=False ) assert_eq( ddf.a.groupby(ddkey).count(), pdf.a.groupby(pdkey).count(), check_names=False, ) assert_eq( ddf.a.groupby(ddkey).mean(), pdf.a.groupby(pdkey).mean(), check_names=False ) assert_eq( ddf.a.groupby(ddkey).nunique(), pdf.a.groupby(pdkey).nunique(), check_names=False, ) assert_eq( ddf.a.groupby(ddkey).first(), pdf.a.groupby(pdkey).first(), check_names=False, ) assert_eq( ddf.a.groupby(ddkey).last(), pdf.a.groupby(pdkey).last(), check_names=False ) assert_eq( ddf.a.groupby(ddkey).prod(), pdf.a.groupby(pdkey).prod(), check_names=False ) for ddof in ddofs: assert_eq(ddf.a.groupby(ddkey).var(ddof), pdf.a.groupby(pdkey).var(ddof)) assert_eq(ddf.a.groupby(ddkey).std(ddof), pdf.a.groupby(pdkey).std(ddof)) for i in [0, 4, 7]: assert_eq(ddf.groupby(ddf.b > i).a.sum(), pdf.groupby(pdf.b > i).a.sum()) assert_eq(ddf.groupby(ddf.b > i).a.min(), pdf.groupby(pdf.b > i).a.min()) assert_eq(ddf.groupby(ddf.b > i).a.max(), pdf.groupby(pdf.b > i).a.max()) assert_eq(ddf.groupby(ddf.b > i).a.count(), pdf.groupby(pdf.b > i).a.count()) assert_eq(ddf.groupby(ddf.b > i).a.mean(), pdf.groupby(pdf.b > i).a.mean()) assert_eq( ddf.groupby(ddf.b > i).a.nunique(), pdf.groupby(pdf.b > i).a.nunique() ) assert_eq(ddf.groupby(ddf.b > i).a.size(), pdf.groupby(pdf.b > i).a.size()) assert_eq(ddf.groupby(ddf.b > i).a.first(), pdf.groupby(pdf.b > i).a.first()) assert_eq(ddf.groupby(ddf.b > i).a.last(), pdf.groupby(pdf.b > i).a.last()) assert_eq(ddf.groupby(ddf.b > i).a.tail(), pdf.groupby(pdf.b > i).a.tail()) assert_eq(ddf.groupby(ddf.b > i).a.head(), pdf.groupby(pdf.b > i).a.head()) assert_eq(ddf.groupby(ddf.b > i).a.prod(), pdf.groupby(pdf.b > i).a.prod()) assert_eq(ddf.groupby(ddf.a > i).b.sum(), pdf.groupby(pdf.a > i).b.sum()) assert_eq(ddf.groupby(ddf.a > i).b.min(), pdf.groupby(pdf.a > i).b.min()) assert_eq(ddf.groupby(ddf.a > i).b.max(), pdf.groupby(pdf.a > i).b.max()) assert_eq(ddf.groupby(ddf.a > i).b.count(), pdf.groupby(pdf.a > i).b.count()) assert_eq(ddf.groupby(ddf.a > i).b.mean(), pdf.groupby(pdf.a > i).b.mean()) assert_eq( ddf.groupby(ddf.a > i).b.nunique(), pdf.groupby(pdf.a > i).b.nunique() ) assert_eq(ddf.groupby(ddf.b > i).b.size(), pdf.groupby(pdf.b > i).b.size()) assert_eq(ddf.groupby(ddf.b > i).b.first(), pdf.groupby(pdf.b > i).b.first()) assert_eq(ddf.groupby(ddf.b > i).b.last(), pdf.groupby(pdf.b > i).b.last()) assert_eq(ddf.groupby(ddf.b > i).b.tail(), pdf.groupby(pdf.b > i).b.tail()) assert_eq(ddf.groupby(ddf.b > i).b.head(), pdf.groupby(pdf.b > i).b.head()) assert_eq(ddf.groupby(ddf.b > i).b.prod(), pdf.groupby(pdf.b > i).b.prod()) assert_eq(ddf.groupby(ddf.b > i).sum(), pdf.groupby(pdf.b > i).sum()) assert_eq(ddf.groupby(ddf.b > i).min(), pdf.groupby(pdf.b > i).min()) assert_eq(ddf.groupby(ddf.b > i).max(), pdf.groupby(pdf.b > i).max()) assert_eq(ddf.groupby(ddf.b > i).count(), pdf.groupby(pdf.b > i).count()) assert_eq(ddf.groupby(ddf.b > i).mean(), pdf.groupby(pdf.b > i).mean()) assert_eq(ddf.groupby(ddf.b > i).size(), pdf.groupby(pdf.b > i).size()) assert_eq(ddf.groupby(ddf.b > i).first(), pdf.groupby(pdf.b > i).first()) assert_eq(ddf.groupby(ddf.b > i).last(), pdf.groupby(pdf.b > i).last()) assert_eq(ddf.groupby(ddf.b > i).prod(), pdf.groupby(pdf.b > i).prod()) assert_eq(ddf.groupby(ddf.a > i).sum(), pdf.groupby(pdf.a > i).sum()) assert_eq(ddf.groupby(ddf.a > i).min(), pdf.groupby(pdf.a > i).min()) assert_eq(ddf.groupby(ddf.a > i).max(), pdf.groupby(pdf.a > i).max()) assert_eq(ddf.groupby(ddf.a > i).count(), pdf.groupby(pdf.a > i).count()) assert_eq(ddf.groupby(ddf.a > i).mean(), pdf.groupby(pdf.a > i).mean()) assert_eq(ddf.groupby(ddf.a > i).size(), pdf.groupby(pdf.a > i).size()) assert_eq(ddf.groupby(ddf.a > i).first(), pdf.groupby(pdf.a > i).first()) assert_eq(ddf.groupby(ddf.a > i).last(), pdf.groupby(pdf.a > i).last()) assert_eq(ddf.groupby(ddf.a > i).prod(), pdf.groupby(pdf.a > i).prod()) for ddof in ddofs: assert_eq( ddf.groupby(ddf.b > i).std(ddof), pdf.groupby(pdf.b > i).std(ddof) ) for ddkey, pdkey in [ ("a", "a"), (ddf.a, pdf.a), (ddf.a + 1, pdf.a + 1), (ddf.a > 3, pdf.a > 3), ]: assert_eq(ddf.groupby(ddkey).b.sum(), pdf.groupby(pdkey).b.sum()) assert_eq(ddf.groupby(ddkey).b.min(), pdf.groupby(pdkey).b.min()) assert_eq(ddf.groupby(ddkey).b.max(), pdf.groupby(pdkey).b.max()) assert_eq(ddf.groupby(ddkey).b.count(), pdf.groupby(pdkey).b.count()) assert_eq(ddf.groupby(ddkey).b.mean(), pdf.groupby(pdkey).b.mean()) assert_eq(ddf.groupby(ddkey).b.nunique(), pdf.groupby(pdkey).b.nunique()) assert_eq(ddf.groupby(ddkey).b.size(), pdf.groupby(pdkey).b.size()) assert_eq(ddf.groupby(ddkey).b.first(), pdf.groupby(pdkey).b.first()) assert_eq(ddf.groupby(ddkey).last(), pdf.groupby(pdkey).last()) assert_eq(ddf.groupby(ddkey).prod(), pdf.groupby(pdkey).prod()) assert_eq(ddf.groupby(ddkey).sum(), pdf.groupby(pdkey).sum()) assert_eq(ddf.groupby(ddkey).min(), pdf.groupby(pdkey).min()) assert_eq(ddf.groupby(ddkey).max(), pdf.groupby(pdkey).max()) assert_eq(ddf.groupby(ddkey).count(), pdf.groupby(pdkey).count()) assert_eq(ddf.groupby(ddkey).mean(), pdf.groupby(pdkey).mean().astype(float)) assert_eq(ddf.groupby(ddkey).size(), pdf.groupby(pdkey).size()) assert_eq(ddf.groupby(ddkey).first(), pdf.groupby(pdkey).first()) assert_eq(ddf.groupby(ddkey).last(), pdf.groupby(pdkey).last()) assert_eq(ddf.groupby(ddkey).prod(), pdf.groupby(pdkey).prod()) for ddof in ddofs: assert_eq(ddf.groupby(ddkey).b.std(ddof), pdf.groupby(pdkey).b.std(ddof)) assert sorted(ddf.groupby("b").a.sum().dask) == sorted( ddf.groupby("b").a.sum().dask ) assert sorted(ddf.groupby(ddf.a > 3).b.mean().dask) == sorted( ddf.groupby(ddf.a > 3).b.mean().dask ) # test raises with incorrect key pytest.raises(KeyError, lambda: ddf.groupby("x")) pytest.raises(KeyError, lambda: ddf.groupby(["a", "x"])) pytest.raises(KeyError, lambda: ddf.groupby("a")["x"]) pytest.raises(KeyError, lambda: ddf.groupby("a")["b", "x"]) pytest.raises(KeyError, lambda: ddf.groupby("a")[["b", "x"]]) if not DASK_EXPR_ENABLED: # test graph node labels assert_dask_graph(ddf.groupby("b").a.sum(), "series-groupby-sum") assert_dask_graph(ddf.groupby("b").a.min(), "series-groupby-min") assert_dask_graph(ddf.groupby("b").a.max(), "series-groupby-max") assert_dask_graph(ddf.groupby("b").a.count(), "series-groupby-count") assert_dask_graph(ddf.groupby("b").a.var(), "series-groupby-var") assert_dask_graph(ddf.groupby("b").a.cov(), "series-groupby-cov") assert_dask_graph(ddf.groupby("b").a.first(), "series-groupby-first") assert_dask_graph(ddf.groupby("b").a.last(), "series-groupby-last") assert_dask_graph(ddf.groupby("b").a.tail(), "series-groupby-tail") assert_dask_graph(ddf.groupby("b").a.head(), "series-groupby-head") assert_dask_graph(ddf.groupby("b").a.prod(), "series-groupby-prod") # mean consists from sum and count operations assert_dask_graph(ddf.groupby("b").a.mean(), "series-groupby-sum") assert_dask_graph(ddf.groupby("b").a.mean(), "series-groupby-count") assert_dask_graph(ddf.groupby("b").a.nunique(), "series-groupby-nunique") assert_dask_graph(ddf.groupby("b").a.size(), "series-groupby-size") assert_dask_graph(ddf.groupby("b").sum(), "dataframe-groupby-sum") assert_dask_graph(ddf.groupby("b").min(), "dataframe-groupby-min") assert_dask_graph(ddf.groupby("b").max(), "dataframe-groupby-max") assert_dask_graph(ddf.groupby("b").count(), "dataframe-groupby-count") assert_dask_graph(ddf.groupby("b").first(), "dataframe-groupby-first") assert_dask_graph(ddf.groupby("b").last(), "dataframe-groupby-last") assert_dask_graph(ddf.groupby("b").prod(), "dataframe-groupby-prod") # mean consists from sum and count operations assert_dask_graph(ddf.groupby("b").mean(), "dataframe-groupby-sum") assert_dask_graph(ddf.groupby("b").mean(), "dataframe-groupby-count") assert_dask_graph(ddf.groupby("b").size(), "dataframe-groupby-size") @pytest.mark.parametrize("keyword", ["split_every", "split_out"]) def test_groupby_reduction_split(keyword, agg_func, shuffle_method): if agg_func in {"first", "last"} and shuffle_method == "disk": pytest.skip(reason="https://github.com/dask/dask/issues/10034") pdf = pd.DataFrame( {"a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 100, "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 100} ) ddf = dd.from_pandas(pdf, npartitions=15) def call(g, m, **kwargs): return getattr(g, m)(**kwargs) # DataFrame # nunique is not implemented for DataFrameGroupBy # covariance/correlation is not a series aggregation if agg_func not in ("nunique", "cov", "corr"): res = call(ddf.groupby("b", sort=False), agg_func, **{keyword: 2}) sol = call(pdf.groupby("b"), agg_func) assert_eq(res, sol) assert call(ddf.groupby("b"), agg_func)._name != res._name if agg_func == "var": res = call(ddf.groupby("b", sort=False), "var", ddof=2, **{keyword: 2}) sol = call(pdf.groupby("b"), "var", ddof=2) assert_eq(res, sol) assert call(ddf.groupby("b"), "var", ddof=2)._name != res._name # Series, post select # covariance/correlation is not a series aggregation if agg_func not in ("cov", "corr"): res = call(ddf.groupby("b", sort=False).a, agg_func, **{keyword: 2}) sol = call(pdf.groupby("b").a, agg_func) assert_eq(res, sol) assert call(ddf.groupby("b").a, agg_func)._name != res._name if agg_func == "var": res = call(ddf.groupby("b", sort=False).a, "var", ddof=2, **{keyword: 2}) sol = call(pdf.groupby("b").a, "var", ddof=2) assert_eq(res, sol) assert call(ddf.groupby("b").a, "var", ddof=2)._name != res._name # Series, pre select # covariance/correlation is not a series aggregation if agg_func not in ("cov", "corr"): res = call(ddf.a.groupby(ddf.b, sort=False), agg_func, **{keyword: 2}) sol = call(pdf.a.groupby(pdf.b), agg_func) # There's a bug in pandas 0.18.0 with `pdf.a.groupby(pdf.b).count()` # not forwarding the series name. Skip name checks here for now. assert_eq(res, sol, check_names=False) assert call(ddf.a.groupby(ddf.b), agg_func)._name != res._name if agg_func == "var": res = call(ddf.a.groupby(ddf.b, sort=False), "var", ddof=2, **{keyword: 2}) sol = call(pdf.a.groupby(pdf.b), "var", ddof=2) assert_eq(res, sol) assert call(ddf.a.groupby(ddf.b), "var", ddof=2)._name != res._name @pytest.mark.parametrize( "grouped", [ lambda df: df.groupby("A"), lambda df: df.groupby(df["A"]), lambda df: df.groupby(df["A"] + 1), lambda df: df.groupby("A")["B"], # SeriesGroupBy: lambda df: df.groupby("A")["B"], lambda df: df.groupby(df["A"])["B"], lambda df: df.groupby(df["A"] + 1)["B"], # Series.groupby(): lambda df: df.B.groupby(df["A"]), lambda df: df.B.groupby(df["A"] + 1), # DataFrameGroupBy with column slice: lambda df: df.groupby("A")[["B", "C"]], lambda df: df.groupby(df["A"])[["B", "C"]], lambda df: df.groupby(df["A"] + 1)[["B", "C"]], ], ) @pytest.mark.parametrize( "func", [ lambda grp: grp.apply(lambda x: x.sum(), **INCLUDE_GROUPS), lambda grp: grp.transform(lambda x: x.sum()), ], ) def test_apply_or_transform_shuffle(grouped, func): pdf = pd.DataFrame( { "A": [1, 2, 3, 4] * 5, "B": np.random.randn(20), "C": np.random.randn(20), "D": np.random.randn(20), } ) ddf = dd.from_pandas(pdf, 3) with pytest.warns(UserWarning): # meta inference assert_eq(func(grouped(pdf)), func(grouped(ddf))) @pytest.mark.parametrize( "grouper", [ lambda df: "AA", lambda df: ["AA", "AB"], lambda df: df["AA"], lambda df: [df["AA"], df["AB"]], lambda df: df["AA"] + 1, pytest.param( lambda df: [df["AA"] + 1, df["AB"] + 1], marks=pytest.mark.xfail(not DASK_EXPR_ENABLED, reason="NotImplemented"), ), ], ) @pytest.mark.parametrize( "func", [ lambda grouped: grouped.apply(lambda x: x.sum(), **INCLUDE_GROUPS), lambda grouped: grouped.transform(lambda x: x.sum()), ], ) def test_apply_or_transform_shuffle_multilevel(grouper, func): pdf = pd.DataFrame( { "AB": [1, 2, 3, 4] * 5, "AA": [1, 2, 3, 4] * 5, "B": np.random.randn(20), "C": np.random.randn(20), "D": np.random.randn(20), } ) ddf = dd.from_pandas(pdf, 3) with pytest.warns(UserWarning): # DataFrameGroupBy assert_eq(func(ddf.groupby(grouper(ddf))), func(pdf.groupby(grouper(pdf)))) # SeriesGroupBy assert_eq( func(ddf.groupby(grouper(ddf))["B"]), func(pdf.groupby(grouper(pdf))["B"]) ) # DataFrameGroupBy with column slice assert_eq( func(ddf.groupby(grouper(ddf))[["B", "C"]]), func(pdf.groupby(grouper(pdf))[["B", "C"]]), ) def test_numeric_column_names(): # df.groupby(0)[df.columns] fails if all columns are numbers (pandas bug) # This ensures that we cast all column iterables to list beforehand. df = pd.DataFrame({0: [0, 1, 0, 1], 1: [1, 2, 3, 4], 2: [0, 1, 0, 1]}) ddf = dd.from_pandas(df, npartitions=2) assert_eq(ddf.groupby(0).sum(), df.groupby(0).sum()) assert_eq(ddf.groupby([0, 2]).sum(), df.groupby([0, 2]).sum()) expected = df.groupby(0).apply(lambda x: x, **INCLUDE_GROUPS) assert_eq( ddf.groupby(0).apply(lambda x: x, meta=expected, **INCLUDE_GROUPS), expected, ) def test_groupby_apply_tasks(shuffle_method): if shuffle_method == "disk": pytest.skip("Tasks-only shuffle test") df = _compat.makeTimeDataFrame() df["A"] = df.A // 0.1 df["B"] = df.B // 0.1 ddf = dd.from_pandas(df, npartitions=10) for ind in [lambda x: "A", lambda x: x.A]: a = df.groupby(ind(df)).apply(len, **INCLUDE_GROUPS) with pytest.warns(UserWarning): b = ddf.groupby(ind(ddf)).apply(len, **INCLUDE_GROUPS) assert_eq(a, b.compute()) assert not any("partd" in k[0] for k in b.dask) a = df.groupby(ind(df)).B.apply(len, **INCLUDE_GROUPS) with pytest.warns(UserWarning): b = ddf.groupby(ind(ddf)).B.apply(len, **INCLUDE_GROUPS) assert_eq(a, b.compute()) assert not any("partd" in k[0] for k in b.dask) def test_groupby_multiprocessing(): df = pd.DataFrame({"A": [1, 2, 3, 4, 5], "B": ["1", "1", "a", "a", "a"]}) ddf = dd.from_pandas(df, npartitions=3) expected = df.groupby("B").apply(lambda x: x, **INCLUDE_GROUPS) # since we explicitly provide meta, we have to convert it to pyarrow strings meta = to_pyarrow_string(expected) if pyarrow_strings_enabled() else expected with dask.config.set(scheduler="processes"): assert_eq( ddf.groupby("B").apply(lambda x: x, meta=meta, **INCLUDE_GROUPS), expected, ) def test_groupby_normalize_by(): full = pd.DataFrame( {"a": [1, 2, 3, 4, 5, 6, 7, 8, 9], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0]}, index=[0, 1, 3, 5, 6, 8, 9, 9, 9], ) d = dd.from_pandas(full, npartitions=3) if DASK_EXPR_ENABLED: assert d.groupby("a").by == ["a"] assert d.groupby(d["a"]).by == ["a"] else: assert d.groupby("a").by == "a" assert d.groupby(d["a"]).by == "a" assert d.groupby(d["a"] > 2).by._name == (d["a"] > 2)._name assert d.groupby(["a", "b"]).by == ["a", "b"] assert d.groupby([d["a"], d["b"]]).by == ["a", "b"] assert d.groupby([d["a"], "b"]).by == ["a", "b"] def test_aggregate__single_element_groups(agg_func): spec = agg_func # nunique/cov is not supported in specs if spec in ("nunique", "cov", "corr"): return pdf = pd.DataFrame( {"a": [1, 1, 3, 3], "b": [4, 4, 16, 16], "c": [1, 1, 4, 4], "d": [1, 1, 3, 3]}, columns=["c", "b", "a", "d"], ) ddf = dd.from_pandas(pdf, npartitions=3) expected = pdf.groupby(["a", "d"]).agg(spec) # NOTE: for std the result is not recast to the original dtype if spec in {"mean", "var"}: expected = expected.astype(float) shuffle_method = ( {"shuffle_method": "tasks", "split_out": 2} if agg_func == "median" else {} ) assert_eq(expected, ddf.groupby(["a", "d"]).agg(spec, **shuffle_method)) def test_aggregate_build_agg_args__reuse_of_intermediates(): """Aggregate reuses intermediates. For example, with sum, count, and mean the sums and counts are only calculated once across the graph and reused to compute the mean. """ from dask.dataframe.groupby import _build_agg_args no_mean_spec = [("foo", "sum", "input"), ("bar", "count", "input")] with_mean_spec = [ ("foo", "sum", "input"), ("bar", "count", "input"), ("baz", "mean", "input"), ] no_mean_chunks, no_mean_aggs, no_mean_finalizers = _build_agg_args(no_mean_spec) with_mean_chunks, with_mean_aggs, with_mean_finalizers = _build_agg_args( with_mean_spec ) assert len(no_mean_chunks) == len(with_mean_chunks) assert len(no_mean_aggs) == len(with_mean_aggs) assert len(no_mean_finalizers) == len(no_mean_spec) assert len(with_mean_finalizers) == len(with_mean_spec) @pytest.mark.parametrize("split_every", [1, 8]) @pytest.mark.parametrize("split_out", [2, 32]) def test_shuffle_aggregate(shuffle_method, split_out, split_every): pdf = pd.DataFrame( { "a": [1, 2, 3, 1, 1, 2, 4, 3, 7] * 100, "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 100, "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 100, "d": [3, 2, 1, 3, 2, 1, 2, 6, 4] * 100, }, columns=["c", "b", "a", "d"], ) ddf = dd.from_pandas(pdf, npartitions=100) spec = {"b": "mean", "c": ["min", "max"]} result = ddf.groupby(["a", "b"], sort=False).agg( spec, split_out=split_out, split_every=split_every, shuffle_method=shuffle_method, ) expect = pdf.groupby(["a", "b"]).agg(spec) # Make sure "mean" dtype is consistent. # Pandas<1.3 will return int instead of float expect[("b", "mean")] = expect[("b", "mean")].astype(result[("b", "mean")].dtype) assert_eq(expect, result) @pytest.mark.parametrize("sort", [True, False]) def test_shuffle_aggregate_sort(shuffle_method, sort): pdf = pd.DataFrame( { "a": [1, 2, 3, 1, 1, 2, 4, 3, 7] * 100, "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 100, "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 100, "d": [3, 2, 1, 3, 2, 1, 2, 6, 4] * 100, }, columns=["c", "b", "a", "d"], ) ddf = dd.from_pandas(pdf, npartitions=100) # Check single-column groupby spec = {"b": "mean", "c": ["min", "max"]} result = ddf.groupby("a", sort=sort).agg( spec, split_out=2, shuffle_backend=shuffle_method ) expect = pdf.groupby("a", sort=sort).agg(spec) assert_eq(expect, result) # Check multi-column groupby result = ddf.groupby(["a", "b"], sort=sort).agg( spec, split_out=2, shuffle_backend=shuffle_method ) expect = pdf.groupby(["a", "b"], sort=sort).agg(spec) assert_eq(expect, result, sort_results=not sort) def test_shuffle_aggregate_defaults(shuffle_method): pdf = pd.DataFrame( { "a": [1, 2, 3, 1, 1, 2, 4, 3, 7] * 100, "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 100, "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 100, "d": [3, 2, 1, 3, 2, 1, 2, 6, 4] * 100, }, columns=["c", "b", "a", "d"], ) ddf = dd.from_pandas(pdf, npartitions=100) spec = {"b": "mean", "c": ["min", "max"]} # No shuffle layer when split_out = 1 dsk = ddf.groupby("a").agg(spec, split_out=1).dask if not DASK_EXPR_ENABLED: assert not any("shuffle" in l for l in dsk.layers) # split_every=1 is invalid for tree reduction with pytest.raises(ValueError): ddf.groupby("a").agg(spec, split_out=1, split_every=1).compute() # If split_out > 1, default to shuffling. dsk = ddf.groupby("a", sort=False).agg(spec, split_out=2, split_every=1).dask if not DASK_EXPR_ENABLED: assert any("shuffle" in l for l in dsk.layers) @pytest.mark.parametrize("spec", [{"c": "median"}, {"b": "median", "c": "max"}]) @pytest.mark.parametrize("keys", ["a", ["a", "d"]]) def test_aggregate_median(spec, keys, shuffle_method): pdf = pd.DataFrame( { "a": [1, 2, 3, 1, 1, 2, 4, 3, 7] * 10, "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10, "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10, "d": [3, 2, 1, 3, 2, 1, 2, 6, 4] * 10, }, columns=["c", "b", "a", "d"], ) ddf = dd.from_pandas(pdf, npartitions=10) actual = ddf.groupby(keys).aggregate(spec, shuffle_method=shuffle_method) expected = pdf.groupby(keys).aggregate(spec) assert_eq(actual, expected) if not DASK_EXPR_ENABLED: with pytest.raises(ValueError, match="must use shuffl"): ddf.groupby(keys).aggregate(spec, shuffle_method=False).compute() with pytest.raises(ValueError, match="must use shuffl"): ddf.groupby(keys).median(shuffle_method=False).compute() @pytest.mark.skipif(DASK_EXPR_ENABLED, reason="deprecated in pandas") @pytest.mark.parametrize("axis", [0, 1]) @pytest.mark.parametrize("group_keys", [True, False, None]) @pytest.mark.parametrize("limit", [None, 1, 4]) def test_fillna(axis, group_keys, limit): df = pd.DataFrame( { "A": [1, 1, 2, 2], "B": [3, 4, 3, 4], "C": [np.nan, 3, np.nan, np.nan], "D": [4, np.nan, 5, np.nan], "E": [6, np.nan, 7, np.nan], } ) ddf = dd.from_pandas(df, npartitions=2) deprecated_ctx = pytest.warns( FutureWarning, match="Please use `ffill`/`bfill` or `fillna` without a GroupBy" ) with groupby_axis_deprecated(): expected = df.groupby("A", group_keys=group_keys).fillna(0, axis=axis) with groupby_axis_deprecated( contextlib.nullcontext() if PANDAS_GE_210 else deprecated_ctx ): result = ddf.groupby("A", group_keys=group_keys).fillna(0, axis=axis) assert_eq(expected, result) with deprecated_ctx: assert_eq( df.groupby("A", group_keys=group_keys).B.fillna(0), ddf.groupby("A", group_keys=group_keys).B.fillna(0), ) with deprecated_ctx: assert_eq( df.groupby(["A", "B"], group_keys=group_keys).fillna(0), ddf.groupby(["A", "B"], group_keys=group_keys).fillna(0), ) with pytest.raises(NotImplementedError), deprecated_ctx: ddf.groupby("A").fillna({"A": 0}) with pytest.raises(NotImplementedError), deprecated_ctx: ddf.groupby("A").fillna(pd.Series(dtype=int)) with pytest.raises(NotImplementedError), deprecated_ctx: ddf.groupby("A").fillna(pd.DataFrame) @pytest.mark.parametrize("group_keys", [True, False, None]) @pytest.mark.parametrize("limit", [None, 1, 4]) def test_ffill(group_keys, limit): df = pd.DataFrame( { "A": [1, 1, 2, 2], "B": [3, 4, 3, 4], "C": [np.nan, 3, np.nan, np.nan], "D": [4, np.nan, 5, np.nan], "E": [6, np.nan, 7, np.nan], } ) ddf = dd.from_pandas(df, npartitions=2) assert_eq( df.groupby("A", group_keys=group_keys).ffill(limit=limit), ddf.groupby("A", group_keys=group_keys).ffill(limit=limit), ) assert_eq( df.groupby("A", group_keys=group_keys).B.ffill(limit=limit), ddf.groupby("A", group_keys=group_keys).B.ffill(limit=limit), ) assert_eq( df.groupby(["A", "B"], group_keys=group_keys).ffill(limit=limit), ddf.groupby(["A", "B"], group_keys=group_keys).ffill(limit=limit), ) @pytest.mark.parametrize("group_keys", [True, False, None]) @pytest.mark.parametrize("limit", [None, 1, 4]) def test_bfill(group_keys, limit): df = pd.DataFrame( { "A": [1, 1, 2, 2], "B": [3, 4, 3, 4], "C": [np.nan, 3, np.nan, np.nan], "D": [np.nan, 4, np.nan, 5], "E": [np.nan, 6, np.nan, 7], } ) ddf = dd.from_pandas(df, npartitions=2) assert_eq( df.groupby("A", group_keys=group_keys).bfill(limit=limit), ddf.groupby("A", group_keys=group_keys).bfill(limit=limit), ) assert_eq( df.groupby("A", group_keys=group_keys).B.bfill(limit=limit), ddf.groupby("A", group_keys=group_keys).B.bfill(limit=limit), ) assert_eq( df.groupby(["A", "B"], group_keys=group_keys).bfill(limit=limit), ddf.groupby(["A", "B"], group_keys=group_keys).bfill(limit=limit), ) @pytest.mark.flaky(reruns=5) # See https://github.com/dask/dask/issues/9793 @pytest.mark.parametrize( "grouper", [ lambda df: ["a"], lambda df: ["a", "b"], lambda df: df["a"], lambda df: [df["a"], df["b"]], lambda df: [df["a"] > 2, df["b"] > 1], ], ) @pytest.mark.parametrize("split_out", [1, 2]) def test_dataframe_aggregations_multilevel(grouper, agg_func, split_out): sort = split_out == 1 # Don't sort for split_out > 1 def call(g, m, **kwargs): return getattr(g, m)(**kwargs) pdf = pd.DataFrame( { "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10, "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10, "d": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10, "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10, }, columns=["c", "b", "a", "d"], ) ddf = dd.from_pandas(pdf, npartitions=10) # covariance only works with N+1 columns if agg_func not in ("cov", "corr"): assert_eq( call(pdf.groupby(grouper(pdf), sort=sort)["c"], agg_func), call( ddf.groupby(grouper(ddf), sort=sort)["c"], agg_func, split_out=split_out, split_every=2, ), ) # not supported by pandas if agg_func != "nunique": if agg_func in ("cov", "corr") and split_out > 1: pytest.skip("https://github.com/dask/dask/issues/9509") assert_eq( call(pdf.groupby(grouper(pdf), sort=sort)[["c", "d"]], agg_func), call( ddf.groupby(grouper(ddf), sort=sort)[["c", "d"]], agg_func, split_out=split_out, split_every=2, ), ) if agg_func in ("cov", "corr"): # there are sorting issues between pandas and chunk cov w/dask df = call(pdf.groupby(grouper(pdf), sort=sort), agg_func).sort_index() cols = sorted(list(df.columns)) df = df[cols] dddf = call( ddf.groupby(grouper(ddf), sort=sort), agg_func, split_out=split_out, split_every=2, ).compute() dddf = dddf.sort_index() cols = sorted(list(dddf.columns)) dddf = dddf[cols] assert_eq(df, dddf) else: assert_eq( call(pdf.groupby(grouper(pdf), sort=sort), agg_func), call( ddf.groupby(grouper(ddf), sort=sort), agg_func, split_out=split_out, split_every=2, ), ) @pytest.mark.parametrize( "grouper", [ lambda df: df["a"], lambda df: [df["a"], df["b"]], lambda df: [df["a"] > 2, df["b"] > 1], ], ) @pytest.mark.parametrize("split_out", [1, 2]) def test_series_aggregations_multilevel(grouper, split_out, agg_func): """ similar to ``test_dataframe_aggregations_multilevel``, but series do not support all groupby args. """ sort = split_out == 1 # Don't sort for split_out > 1 def call(g, m, **kwargs): return getattr(g, m)(**kwargs) # covariance/correlation is not a series aggregation if agg_func in ("cov", "corr"): return pdf = pd.DataFrame( { "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10, "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10, "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10, }, columns=["c", "b", "a"], ) ddf = dd.from_pandas(pdf, npartitions=10) assert_eq( call(pdf["c"].groupby(grouper(pdf), sort=sort), agg_func), call( ddf["c"].groupby(grouper(ddf), sort=sort), agg_func, split_out=split_out, split_every=2, ), # for pandas ~ 0.18, the name is not not properly propagated for # the mean aggregation check_names=(agg_func not in {"mean", "nunique"}), ) @pytest.mark.skipif(DASK_EXPR_ENABLED, reason="don't store nonempty meta in dask-expr") @pytest.mark.parametrize( "grouper", [ lambda df: df["a"], lambda df: df["a"] > 2, lambda df: [df["a"], df["b"]], lambda df: [df["a"] > 2], lambda df: [df["a"] > 2, df["b"] > 1], ], ) @pytest.mark.parametrize( "group_and_slice", [ lambda df, grouper: df.groupby(grouper(df)), lambda df, grouper: df["c"].groupby(grouper(df)), lambda df, grouper: df.groupby(grouper(df))["c"], ], ) def test_groupby_meta_content(group_and_slice, grouper): pdf = pd.DataFrame( { "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10, "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10, "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10, }, columns=["c", "b", "a"], ) ddf = dd.from_pandas(pdf, npartitions=10) expected = group_and_slice(pdf, grouper).first().head(0) meta = group_and_slice(ddf, grouper)._meta.first() meta_nonempty = group_and_slice(ddf, grouper)._meta_nonempty.first().head(0) assert_eq(expected, meta) assert_eq(expected, meta_nonempty) def test_groupy_non_aligned_index(): pdf = pd.DataFrame( { "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10, "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10, "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10, }, columns=["c", "b", "a"], ) ddf3 = dd.from_pandas(pdf, npartitions=3) ddf7 = dd.from_pandas(pdf, npartitions=7) # working examples ddf3.groupby(["a", "b"]) ddf3.groupby([ddf3["a"], ddf3["b"]]) # misaligned divisions with pytest.raises(NotImplementedError): ddf3.groupby(ddf7["a"]) with pytest.raises(NotImplementedError): ddf3.groupby([ddf7["a"], ddf7["b"]]) with pytest.raises(NotImplementedError): ddf3.groupby([ddf7["a"], ddf3["b"]]) with pytest.raises(NotImplementedError): ddf3.groupby([ddf3["a"], ddf7["b"]]) with pytest.raises(NotImplementedError): ddf3.groupby([ddf7["a"], "b"]) def test_groupy_series_wrong_grouper(): df = pd.DataFrame( { "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 10, "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 10, "c": [0, 1, 2, 3, 4, 5, 6, 7, 8] * 10, }, columns=["c", "b", "a"], ) df = dd.from_pandas(df, npartitions=3) s = df["a"] # working index values s.groupby(s) s.groupby([s, s]) # non working index values with pytest.raises(KeyError): s.groupby("foo") with pytest.raises(KeyError): s.groupby([s, "foo"]) with pytest.raises(ValueError): s.groupby(df) with pytest.raises(ValueError): s.groupby([s, df]) @pytest.mark.parametrize("npartitions", [1, 4, 20]) @pytest.mark.parametrize("split_every", [2, 5]) @pytest.mark.parametrize("split_out", [1, 5, 20]) def test_hash_groupby_aggregate(npartitions, split_every, split_out): df = pd.DataFrame({"x": np.arange(100) % 10, "y": np.ones(100)}) ddf = dd.from_pandas(df, npartitions) result = ddf.groupby("x", sort=(split_out == 1)).y.var( split_every=split_every, split_out=split_out ) if not DASK_EXPR_ENABLED: dsk = result.__dask_optimize__(result.dask, result.__dask_keys__()) from dask.core import get_deps dependencies, dependents = get_deps(dsk) assert len([k for k, v in dependencies.items() if not v]) == npartitions assert result.npartitions == split_out assert_eq(result, df.groupby("x").y.var()) def test_split_out_multi_column_groupby(): df = pd.DataFrame( {"x": np.arange(100) % 10, "y": np.ones(100), "z": [1, 2, 3, 4, 5] * 20} ) ddf = dd.from_pandas(df, npartitions=10) result = ddf.groupby(["x", "y"], sort=False).z.mean(split_out=4) expected = df.groupby(["x", "y"]).z.mean() assert_eq(result, expected, check_dtype=False) def test_groupby_split_out_num(): # GH 1841 ddf = dd.from_pandas( pd.DataFrame({"A": [1, 1, 2, 2], "B": [1, 2, 3, 4]}), npartitions=2 ) assert ddf.groupby("A").sum().npartitions == 1 assert ddf.groupby("A", sort=False).sum(split_out=2).npartitions == 2 assert ddf.groupby("A", sort=False).sum(split_out=3).npartitions == 3 with pytest.raises(TypeError): # groupby doesn't accept split_out ddf.groupby("A", split_out=2) def test_groupby_not_supported(): ddf = dd.from_pandas( pd.DataFrame({"A": [1, 1, 2, 2], "B": [1, 2, 3, 4]}), npartitions=2 ) with pytest.raises(TypeError): ddf.groupby("A", axis=1) with pytest.raises(TypeError): ddf.groupby("A", level=1) with pytest.raises(TypeError): ddf.groupby("A", as_index=False) with pytest.raises(TypeError): ddf.groupby("A", squeeze=True) def test_groupby_numeric_column(): df = pd.DataFrame({"A": ["foo", "foo", "bar"], 0: [1, 2, 3]}) ddf = dd.from_pandas(df, npartitions=3) assert_eq(ddf.groupby(ddf.A)[0].sum(), df.groupby(df.A)[0].sum()) @pytest.mark.skipif( WINDOWS and sys.version_info < (3, 11), reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597", ) @pytest.mark.parametrize("sel", ["a", "c", "d", ["a", "b"], ["c", "d"]]) @pytest.mark.parametrize("key", ["a", ["a", "b"]]) @pytest.mark.parametrize("func", ["cumsum", "cumprod", "cumcount"]) def test_cumulative(func, key, sel): df = pd.DataFrame( { "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 6, "b": [4, 2, 7, 3, 3, 1, 1, 1, 2] * 6, "c": np.random.randn(54), "d": np.random.randn(54), }, columns=["a", "b", "c", "d"], ) df.iloc[[-18, -12, -6], -1] = np.nan ddf = dd.from_pandas(df, npartitions=10) g, dg = (d.groupby(key)[sel] for d in (df, ddf)) assert_eq(getattr(g, func)(), getattr(dg, func)()) def test_series_groupby_multi_character_column_name(): df = pd.DataFrame({"aa": [1, 2, 1, 3, 4, 1, 2]}) ddf = dd.from_pandas(df, npartitions=3) assert_eq(df.groupby("aa").aa.cumsum(), ddf.groupby("aa").aa.cumsum()) @pytest.mark.skipif( WINDOWS and sys.version_info < (3, 11), reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597", ) @pytest.mark.skipif(DASK_EXPR_ENABLED, reason="axis doesn't exist in dask-expr") @pytest.mark.parametrize("func", ["cumsum", "cumprod"]) def test_cumulative_axis(func): df = pd.DataFrame( { "a": [1, 2, 6, 4, 4, 6, 4, 3, 7] * 2, "b": np.random.randn(18), "c": np.random.randn(18), } ) df.iloc[-6, -1] = np.nan ddf = dd.from_pandas(df, npartitions=4) # Default expected = getattr(df.groupby("a"), func)() result = getattr(ddf.groupby("a"), func)() assert_eq(expected, result) axis_deprecated = contextlib.nullcontext() if not PANDAS_GE_210: axis_deprecated = pytest.warns( FutureWarning, match="'axis' keyword is deprecated" ) # axis=0 with groupby_axis_deprecated(): expected = getattr(df.groupby("a"), func)(axis=0) with groupby_axis_deprecated(axis_deprecated): result = getattr(ddf.groupby("a"), func)(axis=0) assert_eq(expected, result) # # axis=1 with groupby_axis_deprecated(): expected = getattr(df.groupby("a"), func)(axis=1) with groupby_axis_deprecated(axis_deprecated): result = getattr(ddf.groupby("a"), func)(axis=1) assert_eq(expected, result) with groupby_axis_deprecated( pytest.raises(ValueError, match="No axis named 1 for object type Series"), axis_deprecated, ): getattr(ddf.groupby("a").b, func)(axis=1) with pytest.warns(FutureWarning, match="'axis' keyword is deprecated"): ddf.groupby("a").cumcount(axis=1) def test_groupby_unaligned_index(): df = pd.DataFrame( { "a": np.random.randint(0, 10, 50), "b": np.random.randn(50), "c": np.random.randn(50), } ) ddf = dd.from_pandas(df, npartitions=5) filtered = df[df.b < 0.5] dfiltered = ddf[ddf.b < 0.5] ddf_group = dfiltered.groupby(ddf.a) ds_group = dfiltered.b.groupby(ddf.a) bad = [ ddf_group.mean(), ddf_group.var(), ddf_group.b.nunique(), ddf_group.get_group(0), ds_group.mean(), ds_group.var(), ds_group.nunique(), ds_group.get_group(0), ] for obj in bad: with pytest.raises(ValueError): obj.compute() def add1(x): return x + 1 df_group = filtered.groupby(df.a) expected = df_group.apply(add1, **INCLUDE_GROUPS) assert_eq(ddf_group.apply(add1, meta=expected, **INCLUDE_GROUPS), expected) expected = df_group.b.apply(add1, **INCLUDE_GROUPS) assert_eq(ddf_group.b.apply(add1, meta=expected, **INCLUDE_GROUPS), expected) def test_groupby_string_label(): df = pd.DataFrame({"foo": [1, 1, 4], "B": [2, 3, 4], "C": [5, 6, 7]}) ddf = dd.from_pandas(pd.DataFrame(df), npartitions=1) ddf_group = ddf.groupby("foo") result = ddf_group.get_group(1).compute() expected = pd.DataFrame( {"foo": [1, 1], "B": [2, 3], "C": [5, 6]}, index=pd.Index([0, 1]) ) tm.assert_frame_equal(result, expected) @pytest.mark.skipif( WINDOWS and sys.version_info < (3, 11), reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597", ) @pytest.mark.parametrize("op", ["cumsum", "cumprod"]) def test_groupby_dataframe_cum_caching(op): """Test caching behavior of cumulative operations on grouped dataframes. Relates to #3756. """ df = pd.DataFrame( dict(a=list("aabbcc")), index=pd.date_range(start="20100101", periods=6) ) df["ones"] = 1 df["twos"] = 2 ddf = dd.from_pandas(df, npartitions=3) ddf0 = getattr(ddf.groupby(["a"]), op)() ddf1 = ddf.rename(columns={"ones": "foo", "twos": "bar"}) ddf1 = getattr(ddf1.groupby(["a"]), op)() # _a and _b dataframe should be equal res0_a, res1_a = dask.compute(ddf0, ddf1) res0_b, res1_b = ddf0.compute(), ddf1.compute() assert res0_a.equals(res0_b) assert res1_a.equals(res1_b) @pytest.mark.skipif(DASK_EXPR_ENABLED, reason="MIDX columns not supported") def test_groupby_series_cum_caching(): """Test caching behavior of cumulative operations on grouped Series Relates to #3755 """ df = pd.DataFrame( dict(a=list("aabbcc")), index=pd.date_range(start="20100101", periods=6) ) df["ones"] = 1 df["twos"] = 2 ops = ["cumsum", "cumprod"] for op in ops: ddf = dd.from_pandas(df, npartitions=3) dcum = ddf.groupby(["a"]) res0_a, res1_a = dask.compute( getattr(dcum["ones"], op)(), getattr(dcum["twos"], op)() ) cum = df.groupby(["a"]) res0_b, res1_b = (getattr(cum["ones"], op)(), getattr(cum["twos"], op)()) assert res0_a.equals(res0_b) assert res1_a.equals(res1_b) def test_groupby_slice_agg_reduces(): d = pd.DataFrame({"a": [1, 2, 3, 4], "b": [2, 3, 4, 5]}) a = dd.from_pandas(d, npartitions=2) result = a.groupby("a")["b"].agg(["min", "max"]) expected = d.groupby("a")["b"].agg(["min", "max"]) assert_eq(result, expected) def test_groupby_agg_grouper_single(): # https://github.com/dask/dask/issues/2255 d = pd.DataFrame({"a": [1, 2, 3, 4]}) a = dd.from_pandas(d, npartitions=2) result = a.groupby("a")["a"].agg(["min", "max"]) expected = d.groupby("a")["a"].agg(["min", "max"]) assert_eq(result, expected) @pytest.mark.parametrize("slice_", ["a", ["a"], ["a", "b"], ["b"]]) def test_groupby_agg_grouper_multiple(slice_): # https://github.com/dask/dask/issues/2255 d = pd.DataFrame({"a": [1, 2, 3, 4], "b": [1, 2, 3, 4]}) a = dd.from_pandas(d, npartitions=2) result = a.groupby("a")[slice_].agg(["min", "max"]) expected = d.groupby("a")[slice_].agg(["min", "max"]) assert_eq(result, expected) @pytest.mark.skipif( WINDOWS and sys.version_info < (3, 11), reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597", ) @pytest.mark.parametrize( "agg_func", [ "cumprod", "cumcount", "cumsum", "var", "sum", "mean", "count", "size", "std", "min", "max", "first", "last", "prod", ], ) def test_groupby_column_and_index_agg_funcs(agg_func): def call(g, m, **kwargs): return getattr(g, m)(**kwargs) df = pd.DataFrame( { "idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6), "c": [1, 1, 1, 2, 2, 2], } ).set_index("idx") ddf = dd.from_pandas(df, npartitions=df.index.nunique()) ddf_no_divs = dd.from_pandas(df, npartitions=df.index.nunique(), sort=False) # Index and then column # Compute expected result expected = call(df.groupby(["idx", "a"]), agg_func) if agg_func in {"mean", "var"}: expected = expected.astype(float) result = call(ddf.groupby(["idx", "a"]), agg_func) assert_eq(expected, result) result = call(ddf_no_divs.groupby(["idx", "a"]), agg_func) assert_eq(expected, result) # apply-combine-apply aggregation functions aca_agg = {"sum", "mean", "var", "size", "std", "count", "first", "last", "prod"} # Test aggregate strings if agg_func in aca_agg: result = ddf_no_divs.groupby(["idx", "a"]).agg(agg_func) assert_eq(expected, result) # Column and then index # Compute expected result expected = call(df.groupby(["a", "idx"]), agg_func) if agg_func in {"mean", "var"}: expected = expected.astype(float) result = call(ddf.groupby(["a", "idx"]), agg_func) assert_eq(expected, result) result = call(ddf_no_divs.groupby(["a", "idx"]), agg_func) assert_eq(expected, result) # Test aggregate strings if agg_func in aca_agg: result = ddf_no_divs.groupby(["a", "idx"]).agg(agg_func) assert_eq(expected, result) # Index only # Compute expected result expected = call(df.groupby("idx"), agg_func) if agg_func in {"mean", "var"}: expected = expected.astype(float) result = call(ddf.groupby("idx"), agg_func) assert_eq(expected, result) result = call(ddf_no_divs.groupby("idx"), agg_func) assert_eq(expected, result) # Test aggregate strings if agg_func in aca_agg: result = ddf_no_divs.groupby("idx").agg(agg_func) assert_eq(expected, result) @pytest.mark.parametrize("group_args", [["idx", "a"], ["a", "idx"], ["idx"], "idx"]) @pytest.mark.parametrize( "apply_func", [np.min, np.mean, lambda s, axis=None: np.max(s.values) - np.mean(s.values)], ) def test_groupby_column_and_index_apply(group_args, apply_func): df = pd.DataFrame( {"idx": [1, 1, 1, 2, 2, 2], "a": [1, 2, 1, 2, 1, 2], "b": np.arange(6)} ).set_index("idx") ddf = dd.from_pandas(df, npartitions=df.index.nunique()) ddf_no_divs = dd.from_pandas( df, npartitions=df.index.nunique(), sort=False ).clear_divisions() # Expected result expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS) # Compute on dask DataFrame with divisions (no shuffling) result = ddf.groupby(group_args).apply( apply_func, axis=0, meta=expected, **INCLUDE_GROUPS ) assert_eq(expected, result, check_divisions=False) # Check that partitioning is preserved assert ddf.divisions == result.divisions # Check that no shuffling occurred. # The groupby operation should add only 1 task per partition assert len(result.dask) == (len(ddf.dask) + ddf.npartitions) expected = df.groupby(group_args).apply(apply_func, axis=0, **INCLUDE_GROUPS) # Compute on dask DataFrame without divisions (requires shuffling) result = ddf_no_divs.groupby(group_args).apply( apply_func, axis=0, meta=expected, **INCLUDE_GROUPS ) assert_eq(expected, result, check_divisions=False) # Check that divisions were preserved (all None in this case) assert ddf_no_divs.divisions == result.divisions # Crude check to see if shuffling was performed. # The groupby operation should add only more than 1 task per partition assert len(result.dask) > (len(ddf_no_divs.dask) + ddf_no_divs.npartitions) custom_mean = dd.Aggregation( "mean", lambda s: (s.count(), s.sum()), lambda s0, s1: (s0.sum(), s1.sum()), lambda s0, s1: s1 / s0, ) custom_sum = dd.Aggregation("sum", lambda s: s.sum(), lambda s0: s0.sum()) @pytest.mark.parametrize( "pandas_spec, dask_spec, check_dtype", [ ({"b": "mean"}, {"b": custom_mean}, False), ({"b": "sum"}, {"b": custom_sum}, True), (["mean", "sum"], [custom_mean, custom_sum], False), ({"b": ["mean", "sum"]}, {"b": [custom_mean, custom_sum]}, False), ], ) def test_dataframe_groupby_agg_custom_sum(pandas_spec, dask_spec, check_dtype): df = pd.DataFrame({"g": [0, 0, 1] * 3, "b": [1, 2, 3] * 3}) ddf = dd.from_pandas(df, npartitions=2) expected = df.groupby("g").aggregate(pandas_spec) result = ddf.groupby("g").aggregate(dask_spec) assert_eq(result, expected, check_dtype=check_dtype) @pytest.mark.parametrize( "pandas_spec, dask_spec", [ ("mean", custom_mean), (["mean"], [custom_mean]), (["mean", "sum"], [custom_mean, custom_sum]), ], ) def test_series_groupby_agg_custom_mean(pandas_spec, dask_spec): d = pd.DataFrame({"g": [0, 0, 1] * 3, "b": [1, 2, 3] * 3}) a = dd.from_pandas(d, npartitions=2) expected = d["b"].groupby(d["g"]).aggregate(pandas_spec) result = a["b"].groupby(a["g"]).aggregate(dask_spec) assert_eq(result, expected, check_dtype=False) def test_groupby_agg_custom__name_clash_with_internal_same_column(): """for a single input column only unique names are allowed""" d = pd.DataFrame({"g": [0, 0, 1] * 3, "b": [1, 2, 3] * 3}) a = dd.from_pandas(d, npartitions=2) agg_func = dd.Aggregation("sum", lambda s: s.sum(), lambda s0: s0.sum()) with pytest.raises(ValueError): a.groupby("g").aggregate({"b": [agg_func, "sum"]}) def test_groupby_agg_custom__name_clash_with_internal_different_column(): """custom aggregation functions can share the name of a builtin function""" d = pd.DataFrame({"g": [0, 0, 1] * 3, "b": [1, 2, 3] * 3, "c": [4, 5, 6] * 3}) a = dd.from_pandas(d, npartitions=2) # NOTE: this function is purposefully misnamed agg_func = dd.Aggregation( "sum", lambda s: (s.count(), s.sum()), lambda s0, s1: (s0.sum(), s1.sum()), lambda s0, s1: s1 / s0, ) # NOTE: the name of agg-func is suppressed in the output, # since only a single agg func per column was specified result = a.groupby("g").aggregate({"b": agg_func, "c": "sum"}) expected = d.groupby("g").aggregate({"b": "mean", "c": "sum"}) assert_eq(result, expected, check_dtype=False) def test_groupby_agg_custom__mode(): # mode function passing intermediates as pure python objects around. to protect # results from pandas in apply use return results as single-item lists def agg_mode(s): def impl(s): (res,) = s.iloc[0] for (i,) in s.iloc[1:]: res = res.add(i, fill_value=0) return [res] return s.apply(impl, **INCLUDE_GROUPS) agg_func = dd.Aggregation( "custom_mode", lambda s: s.apply(lambda s: [s.value_counts()], **INCLUDE_GROUPS), agg_mode, lambda s: s.map(lambda i: i[0].idxmax()), ) d = pd.DataFrame( { "g0": [0, 0, 0, 1, 1] * 3, "g1": [0, 0, 0, 1, 1] * 3, "cc": [4, 5, 4, 6, 6] * 3, } ) a = dd.from_pandas(d, npartitions=5) actual = a["cc"].groupby([a["g0"], a["g1"]]).agg(agg_func) # cheat to get the correct index expected = pd.DataFrame({"g0": [0, 1], "g1": [0, 1], "cc": [4, 6]}) expected = expected["cc"].groupby([expected["g0"], expected["g1"]]).agg("sum") assert_eq(actual, expected) @pytest.mark.parametrize("func", ["var", list]) def test_groupby_select_column_agg(func): pdf = pd.DataFrame( { "A": [1, 2, 3, 1, 2, 3, 1, 2, 4], "B": [-0.776, -0.4, -0.873, 0.054, 1.419, -0.948, -0.967, -1.714, -0.666], } ) ddf = dd.from_pandas(pdf, npartitions=4) actual = ddf.groupby("A")["B"].agg(func) expected = pdf.groupby("A")["B"].agg(func) assert_eq(actual, expected) @contextlib.contextmanager def record_numeric_only_warnings(): with warnings.catch_warnings(record=True) as rec: warnings.filterwarnings( "always", "The default value of numeric_only", FutureWarning ) yield rec @pytest.mark.parametrize( "func", [ pytest.param( "var", marks=pytest.mark.xfail(reason="numeric_only=False not implemented"), ), pytest.param( "std", marks=pytest.mark.xfail(reason="numeric_only=False not implemented"), ), pytest.param( "mean", marks=pytest.mark.xfail(reason="numeric_only=False not implemented"), ), pytest.param( "sum", marks=pytest.mark.xfail( pyarrow_strings_enabled() and not PANDAS_GE_230, reason="works in dask-expr", ), ), ], ) def test_std_object_dtype(func): df = pd.DataFrame({"x": [1, 2, 1], "y": ["a", "b", "c"], "z": [11.0, 22.0, 33.0]}) ddf = dd.from_pandas(df, npartitions=2) # DataFrame # TODO: add deprecation warnings to match pandas expected = getattr(df, func)() result = getattr(ddf, func)() assert_eq(expected, result) # DataFrameGroupBy with record_numeric_only_warnings() as rec_pd: expected = getattr(df.groupby("x"), func)() with record_numeric_only_warnings() as rec_dd: result = getattr(ddf.groupby("x"), func)() assert len(rec_pd) == len(rec_dd) assert_eq(expected, result) # SeriesGroupBy with record_numeric_only_warnings() as rec_pd: expected = getattr(df.groupby("x").z, func)() with record_numeric_only_warnings() as rec_dd: result = getattr(ddf.groupby("x").z, func)() assert len(rec_pd) == len(rec_dd) assert_eq(expected, result) def test_std_columns_int(): df = pd.DataFrame({0: [5], 1: [5]}) ddf = dd.from_pandas(df, npartitions=2) if PANDAS_GE_300: assert_eq(ddf.groupby(ddf[0]).std(), df.groupby(df[0]).std()) else: assert_eq(ddf.groupby(ddf[0]).std(), df.groupby(df[0].copy()).std()) def test_timeseries(): df = dask.datasets.timeseries().partitions[:2] assert_eq(df.groupby("name").std(), df.groupby("name").std()) @pytest.mark.parametrize("min_count", [0, 1, 2, 3]) def test_with_min_count(min_count): dfs = [ pd.DataFrame( { "group": ["A", "A", "B"], "val1": [np.nan, 2, 3], "val2": [np.nan, 5, 6], "val3": [5, 4, 9], } ), pd.DataFrame( { "group": ["A", "A", "B"], "val1": [2, np.nan, np.nan], "val2": [np.nan, 5, 6], "val3": [5, 4, 9], } ), ] ddfs = [dd.from_pandas(df, npartitions=4) for df in dfs] for df, ddf in zip(dfs, ddfs): assert_eq( df.groupby("group").sum(min_count=min_count), ddf.groupby("group").sum(min_count=min_count), ) assert_eq( df.groupby("group").prod(min_count=min_count), ddf.groupby("group").prod(min_count=min_count), ) @pytest.mark.parametrize("group_keys", [True, False, None]) def test_groupby_group_keys(group_keys): df = pd.DataFrame({"a": [1, 2, 2, 3], "b": [2, 3, 4, 5]}) ddf = dd.from_pandas(df, npartitions=2).set_index("a") pdf = df.set_index("a") func = lambda g: g.copy() expected = pdf.groupby("a").apply(func, **INCLUDE_GROUPS) assert_eq(expected, ddf.groupby("a").apply(func, meta=expected, **INCLUDE_GROUPS)) expected = pdf.groupby("a", group_keys=group_keys).apply(func, **INCLUDE_GROUPS) assert_eq( expected, ddf.groupby("a", group_keys=group_keys).apply( func, meta=expected, **INCLUDE_GROUPS ), ) @pytest.mark.parametrize( "columns", [["a", "b", "c"], np.array([1.0, 2.0, 3.0]), ["1", "2", "3"], ["", "a", "b"]], ) def test_groupby_cov(columns): rows = 20 cols = 3 data = np.random.randn(rows, cols) df = pd.DataFrame(data, columns=columns) df["key"] = [0] * 10 + [1] * 5 + [2] * 5 ddf = dd.from_pandas(df, npartitions=3) expected = df.groupby("key").cov() result = ddf.groupby("key").cov() # when using numerical values for columns # the column mapping and stacking leads to a float typed # MultiIndex. Pandas will normally create a object typed # MultiIndex if isinstance(columns, np.ndarray): result = result.compute() # don't bother checking index -- MultiIndex levels are in a frozenlist result.columns = result.columns.astype(np.dtype("O")) assert_eq(expected, result, check_index=False) else: assert_eq(expected, result) def test_df_groupby_idxmin(): pdf = pd.DataFrame( {"idx": list(range(4)), "group": [1, 1, 2, 2], "value": [10, 20, 20, 10]} ).set_index("idx") ddf = dd.from_pandas(pdf, npartitions=2) expected = pd.DataFrame({"group": [1, 2], "value": [0, 3]}).set_index("group") result_pd = pdf.groupby("group").idxmin() result_dd = ddf.groupby("group").idxmin() assert_eq(result_pd, result_dd) assert_eq(expected, result_dd) @pytest.mark.skipif(DASK_EXPR_ENABLED, reason="axis not supported") @pytest.mark.parametrize("func", ["idxmin", "idxmax"]) @pytest.mark.parametrize("axis", [0, 1, "index", "columns"]) def test_df_groupby_idx_axis(func, axis): pdf = pd.DataFrame( {"idx": list(range(4)), "group": [1, 1, 2, 2], "value": [10, 20, 20, 10]} ).set_index("idx") ddf = dd.from_pandas(pdf, npartitions=2) warn = None if DASK_EXPR_ENABLED else FutureWarning if axis in (1, "columns"): with ( pytest.raises(NotImplementedError), pytest.warns(warn, match="`axis` parameter is deprecated"), ): getattr(ddf.groupby("group"), func)(axis=axis) else: with groupby_axis_deprecated(dask_op=False): expected = getattr(pdf.groupby("group"), func)(axis=axis) deprecate_ctx = pytest.warns(warn, match="`axis` parameter is deprecated") with groupby_axis_deprecated( contextlib.nullcontext() if PANDAS_GE_210 else deprecate_ctx ): result = getattr(ddf.groupby("group"), func)(axis=axis) assert_eq(expected, result) @pytest.mark.parametrize("skipna", [True, False]) def test_df_groupby_idxmin_skipna(skipna): pdf = pd.DataFrame( { "idx": list(range(4)), "group": [1, 1, 2, 2], "value": [np.nan, 20.1, np.nan, 10.1], } ).set_index("idx") ddf = dd.from_pandas(pdf, npartitions=2) result_dd = ddf.groupby("group").idxmin(skipna=skipna) ctx = contextlib.nullcontext() if not skipna and PANDAS_GE_210 and not PANDAS_GE_300: ctx = pytest.warns(FutureWarning, match="all-NA values") elif not skipna and PANDAS_GE_300: ctx = pytest.raises(ValueError, match="encountered an NA") with ctx: result_pd = pdf.groupby("group").idxmin(skipna=skipna) if not skipna and PANDAS_GE_300: return with ctx: assert_eq(result_pd, result_dd) def test_df_groupby_idxmax(): pdf = pd.DataFrame( {"idx": list(range(4)), "group": [1, 1, 2, 2], "value": [10, 20, 20, 10]} ).set_index("idx") ddf = dd.from_pandas(pdf, npartitions=3) expected = pd.DataFrame({"group": [1, 2], "value": [1, 2]}).set_index("group") result_pd = pdf.groupby("group").idxmax() result_dd = ddf.groupby("group").idxmax() assert_eq(result_pd, result_dd) assert_eq(expected, result_dd) @pytest.mark.parametrize("skipna", [True, False]) def test_df_groupby_idxmax_skipna(skipna): pdf = pd.DataFrame( { "idx": list(range(4)), "group": [1, 1, 2, 2], "value": [np.nan, 20.1, np.nan, 10.1], } ).set_index("idx") ddf = dd.from_pandas(pdf, npartitions=2) result_dd = ddf.groupby("group").idxmax(skipna=skipna) ctx = contextlib.nullcontext() if not skipna and PANDAS_GE_210 and not PANDAS_GE_300: ctx = pytest.warns(FutureWarning, match="all-NA values") elif not skipna and PANDAS_GE_300: ctx = pytest.raises(ValueError, match="encountered an NA") with ctx: result_pd = pdf.groupby("group").idxmax(skipna=skipna) if not skipna and PANDAS_GE_300: return with ctx: assert_eq(result_pd, result_dd) def test_series_groupby_idxmin(): pdf = pd.DataFrame( {"idx": list(range(4)), "group": [1, 1, 2, 2], "value": [10, 20, 20, 10]} ).set_index("idx") ddf = dd.from_pandas(pdf, npartitions=2) expected = ( pd.DataFrame({"group": [1, 2], "value": [0, 3]}).set_index("group").squeeze() ) result_pd = pdf.groupby("group")["value"].idxmin() result_dd = ddf.groupby("group")["value"].idxmin() assert_eq(result_pd, result_dd) assert_eq(expected, result_dd) @pytest.mark.parametrize("skipna", [True, False]) def test_series_groupby_idxmin_skipna(skipna): pdf = pd.DataFrame( { "idx": list(range(4)), "group": [1, 1, 2, 2], "value": [np.nan, 20.1, np.nan, 10.1], } ).set_index("idx") ddf = dd.from_pandas(pdf, npartitions=2) result_dd = ddf.groupby("group")["value"].idxmin(skipna=skipna) ctx = contextlib.nullcontext() if not skipna and PANDAS_GE_210 and not PANDAS_GE_300: ctx = pytest.warns(FutureWarning, match="all-NA values") elif not skipna and PANDAS_GE_300: ctx = pytest.raises(ValueError, match="encountered an NA") with ctx: result_pd = pdf.groupby("group")["value"].idxmin(skipna=skipna) if not skipna and PANDAS_GE_300: return with ctx: assert_eq(result_pd, result_dd) def test_series_groupby_idxmax(): pdf = pd.DataFrame( {"idx": list(range(4)), "group": [1, 1, 2, 2], "value": [10, 20, 20, 10]} ).set_index("idx") ddf = dd.from_pandas(pdf, npartitions=3) expected = ( pd.DataFrame({"group": [1, 2], "value": [1, 2]}).set_index("group").squeeze() ) result_pd = pdf.groupby("group")["value"].idxmax() result_dd = ddf.groupby("group")["value"].idxmax() assert_eq(result_pd, result_dd) assert_eq(expected, result_dd) @pytest.mark.parametrize("skipna", [True, False]) def test_series_groupby_idxmax_skipna(skipna): pdf = pd.DataFrame( { "idx": list(range(4)), "group": [1, 1, 2, 2], "value": [np.nan, 20.1, np.nan, 10.1], } ).set_index("idx") ddf = dd.from_pandas(pdf, npartitions=2) result_dd = ddf.groupby("group")["value"].idxmax(skipna=skipna) ctx = contextlib.nullcontext() if not skipna and PANDAS_GE_210 and not PANDAS_GE_300: ctx = pytest.warns(FutureWarning, match="all-NA values") elif not skipna and PANDAS_GE_300: ctx = pytest.raises(ValueError, match="encountered an NA") with ctx: result_pd = pdf.groupby("group")["value"].idxmax(skipna=skipna) if not skipna and PANDAS_GE_300: return with ctx: assert_eq(result_pd, result_dd) @pytest.mark.skip_with_pyarrow_strings # has to be array to explode @pytest.mark.parametrize("int_dtype", ["uint8", "int32", "int64"]) def test_groupby_unique(int_dtype): rng = np.random.RandomState(42) df = pd.DataFrame( {"foo": rng.randint(3, size=100), "bar": rng.randint(10, size=100)}, dtype=int_dtype, ) ddf = dd.from_pandas(df, npartitions=10) pd_gb = df.groupby("foo")["bar"].unique() dd_gb = ddf.groupby("foo")["bar"].unique() # Use explode because each DataFrame row is a list; equality fails assert_eq(dd_gb.explode(), pd_gb.explode()) @pytest.mark.parametrize("by", ["foo", ["foo", "bar"]]) @pytest.mark.parametrize("int_dtype", ["uint8", "int32", "int64"]) def test_groupby_value_counts(by, int_dtype): rng = np.random.RandomState(42) df = pd.DataFrame( { "foo": rng.randint(3, size=100), "bar": rng.randint(4, size=100), "baz": rng.randint(5, size=100), }, dtype=int_dtype, ) ddf = dd.from_pandas(df, npartitions=2) pd_gb = df.groupby(by).baz.value_counts() dd_gb = ddf.groupby(by).baz.value_counts() assert_eq(dd_gb, pd_gb) def test_groupby_value_counts_10322(): """Repro case for https://github.com/dask/dask/issues/10322.""" df = pd.DataFrame( { "x": [10] * 5 + [6] * 5 + [3] * 5, "y": [1] * 3 + [2] * 3 + [4] * 3 + [5] * 3 + [2] * 3, } ) counts = df.groupby("x")["y"].value_counts() ddf = dd.from_pandas(df, npartitions=3) dcounts = ddf.groupby("x")["y"].value_counts() assert_eq(counts, dcounts) @contextlib.contextmanager def groupby_axis_and_meta(axis=0): # Because we're checking for multiple warnings, we need to record # all warnings and inspect them after the fact with pytest.warns() as record: yield expected_len = 2 if PANDAS_GE_210 and not DASK_EXPR_ENABLED else 1 if axis == 1: expected_len += 1 assert expected_len, [x.message for x in record.list] assert record[-1].category is UserWarning assert "`meta` is not specified" in str(record[-1].message) if PANDAS_GE_210 and not DASK_EXPR_ENABLED: assert record[0].category is FutureWarning assert "axis" in str(record[0].message) @pytest.mark.xfail(DASK_EXPR_ENABLED, reason="axis not supported") @pytest.mark.filterwarnings("ignore:`meta` is not specified") # only in dask-expr @pytest.mark.parametrize("npartitions", [1, 2, 5]) @pytest.mark.parametrize("period", [1, -1, 10]) @pytest.mark.parametrize("axis", [0, 1]) def test_groupby_shift_basic_input(npartitions, period, axis): pdf = pd.DataFrame( { "a": [0, 0, 1, 1, 2, 2, 3, 3, 3], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0], "c": [0, 0, 0, 0, 0, 1, 1, 1, 1], }, ) ddf = dd.from_pandas(pdf, npartitions=npartitions) with groupby_axis_deprecated(dask_op=False): expected = pdf.groupby(["a", "c"]).shift(period, axis=axis) with groupby_axis_and_meta(axis): result = ddf.groupby(["a", "c"]).shift(period, axis=axis) assert_eq(expected, result) if DASK_EXPR_ENABLED: ctx = contextlib.nullcontext() else: ctx = pytest.warns(FutureWarning, match="`axis` parameter is deprecated") with ctx: ddf.groupby(["a", "c"]).shift(period, axis=axis) with groupby_axis_deprecated(dask_op=False): expected = pdf.groupby(["a"]).shift(period, axis=axis) with groupby_axis_and_meta(axis): result = ddf.groupby(["a"]).shift(period, axis=axis) assert_eq(expected, result) with groupby_axis_deprecated(dask_op=False): expected = pdf.groupby(pdf.c).shift(period, axis=axis) with groupby_axis_and_meta(axis): result = ddf.groupby(ddf.c).shift(period, axis=axis) assert_eq(expected, result) with ctx: ddf.groupby(ddf.c).shift(period, axis=axis) def test_groupby_shift_series(): pdf = pd.DataFrame( { "a": [0, 0, 1, 1, 2, 2, 3, 3, 3], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0], }, ) ddf = dd.from_pandas(pdf, npartitions=3) with pytest.warns(UserWarning): assert_eq( pdf.groupby("a")["b"].shift(periods=2), ddf.groupby("a")["b"].shift(periods=2), ) @pytest.mark.xfail(DASK_EXPR_ENABLED, reason="delayed not currently supported in here") def test_groupby_shift_lazy_input(): pdf = pd.DataFrame( { "a": [0, 0, 1, 1, 2, 2, 3, 3, 3], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0], "c": [0, 0, 0, 0, 0, 1, 1, 1, 1], }, ) delayed_periods = dask.delayed(lambda: 1)() ddf = dd.from_pandas(pdf, npartitions=3) assert_eq( pdf.groupby(pdf.c).shift(periods=1), ddf.groupby(ddf.c).shift(periods=delayed_periods, meta={"a": int, "b": int}), ) with pytest.warns(UserWarning): assert_eq( pdf.groupby(pdf.c).shift(periods=1, fill_value=pdf.b.max()), ddf.groupby(ddf.c).shift(periods=1, fill_value=ddf.b.max()), ) @pytest.mark.skipif( not DASK_EXPR_ENABLED, reason="flaky with a weird deprecation warning" ) @pytest.mark.filterwarnings("ignore:`meta` is not specified") def test_groupby_shift_within_partition_sorting(): # Result is non-deterministic. We run the assertion a few times to keep # the probability of false pass low. for _ in range(10): df = pd.DataFrame( { "a": range(60), "b": [2, 4, 3, 1] * 15, "c": [None, 10, 20, None, 30, 40] * 10, } ) df = df.set_index("a").sort_index() ddf = dd.from_pandas(df, npartitions=6) assert_eq( df.groupby("b")["c"].shift(1), ddf.groupby("b")["c"].shift(1), scheduler="threads", ) def test_groupby_shift_with_freq(shuffle_method): pdf = pd.DataFrame( dict(a=[1, 2, 3, 4, 5, 6], b=[0, 0, 0, 1, 1, 1]), index=pd.date_range(start="20100101", periods=6), ) ddf = dd.from_pandas(pdf, npartitions=3) # just pass the pandas result as meta for convenience df_result = pdf.groupby(pdf.index).shift(periods=-2, freq="D") # Groupby/shift on the index should avoid shuffle and let the `freq` pass # unmodified, but that is currently broken: https://github.com/dask/dask/issues/8959 # TODO: remove check_freq condition once fixed. assert_eq( df_result, ddf.groupby(ddf.index).shift(periods=-2, freq="D", meta=df_result), check_freq=False, ) df_result = pdf.groupby("b").shift(periods=-2, freq="D") assert_eq( df_result, ddf.groupby("b").shift(periods=-2, freq="D", meta=df_result), # Somehow https://github.com/dask/dask/issues/10034 causes us to # drop the `freq` check_freq=shuffle_method != "disk", ) @pytest.mark.parametrize( "transformation", [lambda x: x.sum(), np.sum, "sum", pd.Series.rank] ) def test_groupby_transform_funcs(transformation): pdf = pd.DataFrame( { "A": [1, 2, 3, 4] * 5, "B": np.random.randn(20), "C": np.random.randn(20), "D": np.random.randn(20), } ) ddf = dd.from_pandas(pdf, 3) with pytest.warns(UserWarning): # DataFrame assert_eq( pdf.groupby("A").transform(transformation), ddf.groupby("A").transform(transformation), ) # Series assert_eq( pdf.groupby("A")["B"].transform(transformation), ddf.groupby("A")["B"].transform(transformation), ) @pytest.mark.parametrize("npartitions", list(range(1, 10))) @pytest.mark.parametrize("indexed", [True, False], ids=["indexed", "not_indexed"]) def test_groupby_transform_ufunc_partitioning(npartitions, indexed): pdf = pd.DataFrame({"group": [1, 2, 3, 4, 5] * 20, "value": np.random.randn(100)}) if indexed: pdf = pdf.set_index("group") ddf = dd.from_pandas(pdf, npartitions) with pytest.warns(UserWarning): # DataFrame assert_eq( pdf.groupby("group").transform(lambda series: series - series.mean()), ddf.groupby("group").transform(lambda series: series - series.mean()), ) # Series assert_eq( pdf.groupby("group")["value"].transform( lambda series: series - series.mean() ), ddf.groupby("group")["value"].transform( lambda series: series - series.mean() ), ) @pytest.mark.parametrize( "grouping,agg", [ ( lambda df: df.drop(columns="category_2").groupby("category_1"), lambda grp: grp.mean(), ), ( lambda df: df.drop(columns="category_2").groupby("category_1"), lambda grp: grp.agg("mean"), ), ( lambda df: df.groupby(["category_1", "category_2"]), lambda grp: grp.mean(), ), ( lambda df: df.groupby(["category_1", "category_2"]), lambda grp: grp.agg("mean"), ), ], ) def test_groupby_aggregate_categoricals(grouping, agg): pdf = pd.DataFrame( { "category_1": pd.Categorical(list("AABBCC")), "category_2": pd.Categorical(list("ABCABC")), "value": np.random.uniform(size=6), } ) ddf = dd.from_pandas(pdf, 2) # DataFrameGroupBy with check_observed_deprecation(): expected = agg(grouping(pdf)) observed_ctx = ( pytest.warns(FutureWarning, match="observed") if PANDAS_GE_210 and not PANDAS_GE_300 else contextlib.nullcontext() ) with observed_ctx: result = agg(grouping(ddf)) assert_eq(result, expected) # SeriesGroupBy with check_observed_deprecation(): expected = agg(grouping(pdf)["value"]) with observed_ctx: result = agg(grouping(ddf)["value"]) assert_eq(result, expected) @pytest.mark.parametrize( "agg", [ lambda grp: grp.agg(partial(np.std, ddof=1)), lambda grp: grp.agg(partial(np.std, ddof=-2)), lambda grp: grp.agg(partial(np.var, ddof=1)), lambda grp: grp.agg(partial(np.var, ddof=-2)), ], ) def test_groupby_aggregate_partial_function(agg): pdf = pd.DataFrame( { "a": [5, 4, 3, 5, 4, 2, 3, 2], "b": [1, 2, 5, 6, 9, 2, 6, 8], } ) ddf = dd.from_pandas(pdf, npartitions=2) # DataFrameGroupBy assert_eq(agg(pdf.groupby("a")), agg(ddf.groupby("a"))) # SeriesGroupBy assert_eq(agg(pdf.groupby("a")["b"]), agg(ddf.groupby("a")["b"])) @pytest.mark.parametrize( "agg", [ lambda grp: grp.agg(partial(np.std, unexpected_arg=1)), lambda grp: grp.agg(partial(np.var, unexpected_arg=1)), ], ) def test_groupby_aggregate_partial_function_unexpected_kwargs(agg): pdf = pd.DataFrame( { "a": [5, 4, 3, 5, 4, 2, 3, 2], "b": [1, 2, 5, 6, 9, 2, 6, 8], } ) ddf = dd.from_pandas(pdf, npartitions=2) with pytest.raises( TypeError, match=( "supports {'ddof'} keyword arguments, but got {'unexpected_arg'}|" "unexpected keyword argument 'unexpected_arg'" ), ): agg(ddf.groupby("a")) # SeriesGroupBy with pytest.raises( TypeError, match=( "supports {'ddof'} keyword arguments, but got {'unexpected_arg'}|" "unexpected keyword argument 'unexpected_arg'" ), ): agg(ddf.groupby("a")["b"]) @pytest.mark.parametrize( "agg", [ lambda grp: grp.agg(partial(np.std, "positional_arg")), lambda grp: grp.agg(partial(np.var, "positional_arg")), ], ) def test_groupby_aggregate_partial_function_unexpected_args(agg): pdf = pd.DataFrame( { "a": [5, 4, 3, 5, 4, 2, 3, 2], "b": [1, 2, 5, 6, 9, 2, 6, 8], } ) ddf = dd.from_pandas(pdf, npartitions=2) with pytest.raises( TypeError, match=( "doesn't support positional arguments" "|'Series' object cannot be interpreted as an integer" "|cannot convert the series to " ), ): agg(ddf.groupby("a")) # SeriesGroupBy with pytest.raises( TypeError, match=( "doesn't support positional arguments" "|'Series' object cannot be interpreted as an integer" "|cannot convert the series to " ), ): agg(ddf.groupby("a")["b"]) @pytest.mark.parametrize("dropna", [False, True]) def test_groupby_dropna_pandas(dropna): df = pd.DataFrame( {"a": [1, 2, 3, 4, None, None, 7, 8], "e": [4, 5, 6, 3, 2, 1, 0, 0]} ) ddf = dd.from_pandas(df, npartitions=3) dask_result = ddf.groupby("a", dropna=dropna).e.sum() pd_result = df.groupby("a", dropna=dropna).e.sum() assert_eq(dask_result, pd_result) @pytest.mark.gpu @pytest.mark.parametrize("dropna", [False, True, None]) @pytest.mark.parametrize("by", ["a", "c", "d", ["a", "b"], ["a", "c"], ["a", "d"]]) @pytest.mark.parametrize( "group_keys", [ True, False, None, ], ) def test_groupby_dropna_cudf(dropna, by, group_keys): # NOTE: This test requires cudf/dask_cudf, and will # be skipped by non-GPU CI cudf = pytest.importorskip("cudf") dask_cudf = pytest.importorskip("dask_cudf") df = cudf.DataFrame( { "a": [1, 2, 3, 4, None, None, 7, 8], "b": [1, 0] * 4, "c": ["a", "b", None, None, "e", "f", "g", "h"], "e": [4, 5, 6, 3, 2, 1, 0, 0], } ) df["d"] = df["c"].astype("category") ddf = dask_cudf.from_cudf(df, npartitions=3) if dropna is None: dask_result = ddf.groupby(by, group_keys=group_keys).e.sum() cudf_result = df.groupby(by, group_keys=group_keys).e.sum() else: dask_result = ddf.groupby(by, dropna=dropna, group_keys=group_keys).e.sum() cudf_result = df.groupby(by, dropna=dropna, group_keys=group_keys).e.sum() if by in ["c", "d"]: # Lose string/category index name in cudf... dask_result = dask_result.compute() dask_result.index.name = cudf_result.index.name assert_eq(dask_result, cudf_result) @pytest.mark.gpu @pytest.mark.parametrize("key", ["a", "b"]) def test_groupby_grouper_dispatch(key): cudf = pytest.importorskip("cudf") # not directly used but must be imported pytest.importorskip("dask_cudf") # noqa: F841 pdf = pd.DataFrame( { "a": ["a", "b", "c", "d", "e", "f", "g", "h"], "b": [1, 2, 3, 4, 5, 6, 7, 8], "c": [1.0, 2.0, 3.5, 4.1, 5.5, 6.6, 7.9, 8.8], } ) gdf = cudf.from_pandas(pdf) pd_grouper = grouper_dispatch(pdf)(key=key) gd_grouper = grouper_dispatch(gdf)(key=key) expect = pdf.groupby(pd_grouper).sum(numeric_only=True) got = gdf.groupby(gd_grouper).sum(numeric_only=True) assert_eq(expect, got) @pytest.mark.gpu @pytest.mark.parametrize( "group_keys", [ True, False, ], ) def test_groupby_apply_cudf(group_keys): # Check that groupby-apply is consistent between # 'pandas' and 'cudf' backends, and that the # implied shuffle works for the `cudf` backend # Make sure test is skipped without dask_cudf pytest.importorskip("dask_cudf") # noqa: F841 cudf = pytest.importorskip("cudf") df = pd.DataFrame({"a": [1, 2, 3, 1, 2, 3], "b": [4, 5, 6, 7, 8, 9]}) ddf = dd.from_pandas(df, npartitions=2) dcdf = ddf.to_backend("cudf") func = lambda x: x res_pd = df.groupby("a", group_keys=group_keys).apply(func, **INCLUDE_GROUPS) res_dd = ddf.groupby("a", group_keys=group_keys).apply( func, meta=res_pd, **INCLUDE_GROUPS ) res_dc = dcdf.groupby("a", group_keys=group_keys).apply( func, meta=cudf.from_pandas(res_pd), **INCLUDE_GROUPS ) assert_eq(res_pd, res_dd) # Pandas and cudf return different `index.name` # for empty MultiIndex (use `check_names=False`) assert_eq(res_dd, res_dc, check_names=False) @pytest.mark.parametrize("sort", [True, False]) def test_groupby_dropna_with_agg(sort): # https://github.com/dask/dask/issues/6986 df = pd.DataFrame( {"id1": ["a", None, "b"], "id2": [1, 2, None], "v1": [4.5, 5.5, None]} ) expected = df.groupby(["id1", "id2"], dropna=False, sort=sort).agg("sum") ddf = dd.from_pandas(df, 1) actual = ddf.groupby(["id1", "id2"], dropna=False, sort=sort).agg("sum") assert_eq(expected, actual) def test_groupby_observed_with_agg(): df = pd.DataFrame( { "cat_1": pd.Categorical(list("AB"), categories=list("ABCDE")), "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3]), "value_1": np.random.uniform(size=2), } ) expected = df.groupby(["cat_1", "cat_2"], observed=True).agg("sum") ddf = dd.from_pandas(df, 2) actual = ddf.groupby(["cat_1", "cat_2"], observed=True).agg("sum") assert_eq(expected, actual) def test_rounding_negative_var(): x = [-0.00179999999 for _ in range(10)] ids = [1 for _ in range(5)] + [2 for _ in range(5)] df = pd.DataFrame({"ids": ids, "x": x}) ddf = dd.from_pandas(df, npartitions=2) assert_eq(ddf.groupby("ids").x.std(), df.groupby("ids").x.std()) @pytest.mark.parametrize("split_out", [2, 3]) @pytest.mark.parametrize("column", [["b", "c"], ["b", "d"], ["b", "e"]]) def test_groupby_split_out_multiindex(split_out, column): df = pd.DataFrame( { "a": np.arange(8), "b": [1, 0, 0, 2, 1, 1, 2, 0], "c": [0, 1] * 4, "d": ["dog", "cat", "cat", "dog", "dog", "dog", "cat", "bird"], } ).fillna(0) df["e"] = df["d"].astype("category") ddf = dd.from_pandas(df, npartitions=3) if column == ["b", "e"] and PANDAS_GE_210 and not PANDAS_GE_300: ctx = pytest.warns(FutureWarning, match="observed") else: ctx = contextlib.nullcontext() with ctx: result_so1 = ( ddf.groupby(column, sort=False).a.mean(split_out=1).compute().dropna() ) result = ( ddf.groupby(column, sort=False) .a.mean(split_out=split_out) .compute() .dropna() ) assert_eq(result, result_so1) @pytest.mark.parametrize( "backend", [ "pandas", pytest.param("cudf", marks=pytest.mark.gpu), ], ) def test_groupby_large_ints_exception(backend): data_source = pytest.importorskip(backend) if backend == "cudf": dask_cudf = pytest.importorskip("dask_cudf") data_frame = dask_cudf.from_cudf else: data_frame = dd.from_pandas max = np.iinfo(np.uint64).max sqrt = max**0.5 series = data_source.Series( np.concatenate([sqrt * np.arange(5), np.arange(35)]) ).astype("int64") df = data_source.DataFrame({"x": series, "z": np.arange(40), "y": np.arange(40)}) ddf = data_frame(df, npartitions=1) assert_eq( df.groupby("x").std(), ddf.groupby("x").std().compute(scheduler="single-threaded"), ) @pytest.mark.parametrize("by", ["a", "b", "c", ["a", "b"], ["a", "c"]]) # TODO: Remove the need for `strict=False` below @pytest.mark.parametrize( "agg", [ "count", pytest.param( "mean", marks=pytest.mark.xfail( reason="numeric_only=False not implemented", strict=False ), ), pytest.param( "std", marks=pytest.mark.xfail( reason="numeric_only=False not implemented", strict=False ), ), ], ) @pytest.mark.parametrize("sort", [True, False]) def test_groupby_sort_argument(by, agg, sort): df = pd.DataFrame( { "a": [1, 2, 3, 4, None, None, 7, 8], "b": [1, 0] * 4, "c": ["a", "b", None, None, "e", "f", "g", "h"], "e": [4, 5, 6, 3, 2, 1, 0, 0], } ) ddf = dd.from_pandas(df, npartitions=3) gb = ddf.groupby(by, sort=sort) gb_pd = df.groupby(by, sort=sort) # Basic groupby aggregation result_1 = getattr(gb, agg) result_1_pd = getattr(gb_pd, agg) # Choose single column result_2 = getattr(gb.e, agg) result_2_pd = getattr(gb_pd.e, agg) # Use `agg()` api result_3 = gb.agg({"e": agg}) result_3_pd = gb_pd.agg({"e": agg}) if agg == "mean": with record_numeric_only_warnings() as rec_pd: expected = result_1_pd().astype("float") with record_numeric_only_warnings() as rec_dd: result = result_1() assert len(rec_pd) == len(rec_dd) assert_eq(result, expected) with record_numeric_only_warnings() as rec_pd: expected = result_2_pd().astype("float") with record_numeric_only_warnings() as rec_dd: result = result_2() assert len(rec_pd) == len(rec_dd) assert_eq(result, expected) with record_numeric_only_warnings() as rec_pd: expected = result_3_pd.astype("float") with record_numeric_only_warnings() as rec_dd: result = result_3 assert len(rec_pd) == len(rec_dd) assert_eq(result, expected) else: with record_numeric_only_warnings() as rec_pd: expected = result_1_pd() with record_numeric_only_warnings() as rec_dd: result = result_1() assert len(rec_pd) == len(rec_dd) assert_eq(result, expected) with record_numeric_only_warnings() as rec_pd: expected = result_2_pd() with record_numeric_only_warnings() as rec_dd: result = result_2() assert len(rec_pd) == len(rec_dd) assert_eq(result, expected) with record_numeric_only_warnings() as rec_pd: expected = result_3_pd with record_numeric_only_warnings() as rec_dd: result = result_3 assert len(rec_pd) == len(rec_dd) assert_eq(result, expected) @pytest.mark.parametrize("agg", [M.sum, M.prod, M.max, M.min]) @pytest.mark.parametrize("sort", [True, False]) def test_groupby_sort_argument_agg(agg, sort): df = pd.DataFrame({"x": [4, 2, 1, 2, 3, 1], "y": [1, 2, 3, 4, 5, 6]}) ddf = dd.from_pandas(df, npartitions=3) result = agg(ddf.groupby("x", sort=sort)) result_pd = agg(df.groupby("x", sort=sort)) assert_eq(result, result_pd) if sort: # Check order of index if sort==True # (no guarantee that order will match otherwise) assert_eq(result.index, result_pd.index) def test_groupby_sort_true_split_out(): df = pd.DataFrame({"x": [4, 2, 1, 2, 3, 1], "y": [1, 2, 3, 4, 5, 6]}) ddf = dd.from_pandas(df, npartitions=3) # Works fine for split_out==1 or sort=False/None M.sum(ddf.groupby("x", sort=True), split_out=1) M.sum(ddf.groupby("x", sort=False), split_out=2) ddf.groupby("x").sum(split_out=2) ddf.groupby("x").agg("sum", split_out=2) if not DASK_EXPR_ENABLED: with pytest.raises(NotImplementedError): # Cannot use sort=True with split_out>1 using non-shuffle-based approach M.sum(ddf.groupby("x", sort=True), shuffle_method=False, split_out=2) # Can use sort=True with split_out>1 with agg() if shuffle=True ddf.groupby("x", sort=True).agg("sum", split_out=2, shuffle_method=True) @pytest.mark.parametrize("known_cats", [True, False], ids=["known", "unknown"]) @pytest.mark.parametrize("ordered_cats", [True, False], ids=["ordered", "unordererd"]) @pytest.mark.parametrize("groupby", ["cat_1", ["cat_1", "cat_2"]]) @pytest.mark.parametrize("observed", [True, False], ids=["observed", "unobserved"]) def test_groupby_aggregate_categorical_observed( known_cats, ordered_cats, agg_func, groupby, observed ): if agg_func in ["cov", "corr", "nunique"]: pytest.skip("Not implemented for DataFrameGroupBy yet.") if agg_func == "median" and isinstance(groupby, str): pytest.skip("Can't calculate median over categorical") if agg_func == "median" and DASK_EXPR_ENABLED: pytest.skip("Can't deal with unobserved cats in median at the moment") if agg_func in ["sum", "count", "prod"] and groupby != "cat_1": pytest.skip("Gives zeros rather than nans.") if agg_func in ["std", "var"] and observed: pytest.skip("Can't calculate observed with all nans") if agg_func in ["sum", "prod"]: pytest.xfail("Not implemented for category type with pandas 2.0") pdf = pd.DataFrame( { "cat_1": pd.Categorical( list("AB"), categories=list("ABCDE"), ordered=ordered_cats ), "cat_2": pd.Categorical([1, 2], categories=[1, 2, 3], ordered=ordered_cats), "value_1": np.random.uniform(size=2), } ) ddf = dd.from_pandas(pdf, 2) if not known_cats: ddf["cat_1"] = ddf["cat_1"].cat.as_unknown() ddf["cat_2"] = ddf["cat_2"].cat.as_unknown() def agg(grp, **kwargs): return getattr(grp, agg_func)(**kwargs) # only include numeric columns when passing to "min" or "max" # pandas default is numeric_only=False if ordered_cats is False and agg_func in ["min", "max"] and groupby == "cat_1": pdf = pdf[["cat_1", "value_1"]] ddf = ddf[["cat_1", "value_1"]] assert_eq( agg(pdf.groupby(groupby, observed=observed)), agg(ddf.groupby(groupby, observed=observed)), ) def test_groupby_cov_non_numeric_grouping_column(): pdf = pd.DataFrame( { "a": 1, "b": [ pd.Timestamp("2019-12-31"), pd.Timestamp("2019-12-31"), pd.Timestamp("2019-12-31"), ], "c": 2, } ) ddf = dd.from_pandas(pdf, npartitions=2) if DASK_EXPR_ENABLED: assert_eq(ddf.groupby("b").cov(numeric_only=True), pdf.groupby("b").cov()) else: assert_eq(ddf.groupby("b").cov(), pdf.groupby("b").cov()) def test_groupby_numeric_only_None_column_name(): df = pd.DataFrame({"a": [1, 2, 3], None: ["a", "b", "c"]}) ddf = dd.from_pandas(df, npartitions=1) with pytest.raises(NotImplementedError): ddf.groupby(lambda x: x).mean(numeric_only=False) @pytest.mark.parametrize("shuffle_method", [True, False]) def test_dataframe_named_agg(shuffle_method): df = pd.DataFrame( { "a": [1, 1, 2, 2], "b": [1, 2, 5, 6], "c": [6, 3, 6, 7], } ) ddf = dd.from_pandas(df, npartitions=2) expected = df.groupby("a").agg( x=pd.NamedAgg("b", aggfunc="sum"), y=pd.NamedAgg("c", aggfunc=partial(np.std, ddof=1)), ) actual = ddf.groupby("a").agg( shuffle_method=shuffle_method, x=pd.NamedAgg("b", aggfunc="sum"), y=pd.NamedAgg("c", aggfunc=partial(np.std, ddof=1)), ) assert_eq(expected, actual) @pytest.mark.parametrize("shuffle_method", [True, False]) @pytest.mark.parametrize("agg", ["count", "mean", partial(np.var, ddof=1)]) def test_series_named_agg(shuffle_method, agg): df = pd.DataFrame( { "a": [5, 4, 3, 5, 4, 2, 3, 2], "b": [1, 2, 5, 6, 9, 2, 6, 8], } ) ddf = dd.from_pandas(df, npartitions=2) expected = df.groupby("a").b.agg(c=agg, d="sum") actual = ddf.groupby("a").b.agg(shuffle_method=shuffle_method, c=agg, d="sum") assert_eq(expected, actual) @pytest.mark.parametrize("by", ["A", ["A", "B"]]) def test_empty_partitions_with_value_counts(by): # https://github.com/dask/dask/issues/7065 df = pd.DataFrame( data=[ ["a1", "b1", True], ["a1", None, False], ["a1", "b1", True], [None, None, None], [None, None, None], [None, None, None], ["a3", "b3", True], ["a3", "b3", False], ["a5", "b5", True], ], columns=["A", "B", "C"], ) if pyarrow_strings_enabled(): df = df.convert_dtypes() expected = df.groupby(by).C.value_counts() ddf = dd.from_pandas(df, npartitions=3) actual = ddf.groupby(by).C.value_counts() assert_eq(expected, actual) def test_groupby_with_pd_grouper(): ddf = dd.from_pandas( pd.DataFrame( {"key1": ["a", "b", "a"], "key2": ["c", "c", "c"], "value": [1, 2, 3]} ), npartitions=3, ) with pytest.raises(NotImplementedError): ddf.groupby(pd.Grouper(key="key1")) with pytest.raises(NotImplementedError): ddf.groupby(["key1", pd.Grouper(key="key2")]) # TODO: Remove filter once https://github.com/pandas-dev/pandas/issues/46814 is resolved @pytest.mark.filterwarnings("ignore:Invalid value encountered:RuntimeWarning") @pytest.mark.parametrize("operation", ["head", "tail"]) def test_groupby_empty_partitions_with_rows_operation(operation): df = pd.DataFrame( data=[ ["a1", "b1"], ["a1", None], ["a1", "b1"], [None, None], [None, None], [None, None], ["a3", "b3"], ["a3", "b3"], ["a5", "b5"], ], columns=["A", "B"], ) caller = operator.methodcaller(operation, 1) expected = caller(df.groupby("A")["B"]) ddf = dd.from_pandas(df, npartitions=3) actual = caller(ddf.groupby("A")["B"]) assert_eq(expected, actual) @pytest.mark.parametrize("operation", ["head", "tail"]) def test_groupby_with_row_operations(operation): df = pd.DataFrame( data=[ ["a0", "b1"], ["a0", "b2"], ["a1", "b1"], ["a3", "b3"], ["a3", "b3"], ["a5", "b5"], ["a1", "b1"], ["a1", "b1"], ["a1", "b1"], ], columns=["A", "B"], ) caller = operator.methodcaller(operation) expected = caller(df.groupby("A")["B"]) ddf = dd.from_pandas(df, npartitions=3) actual = caller(ddf.groupby("A")["B"]) assert_eq(expected, actual) @pytest.mark.parametrize("operation", ["head", "tail"]) def test_groupby_multi_index_with_row_operations(operation): df = pd.DataFrame( data=[ ["a0", "b1"], ["a0", "b2"], ["a1", "b1"], ["a3", "b3"], ["a3", "b3"], ["a5", "b5"], ["a1", "b1"], ["a1", "b1"], ["a1", "b1"], ], columns=["A", "B"], ) caller = operator.methodcaller(operation) expected = caller(df.groupby(["A", df["A"].eq("a1")])["B"]) ddf = dd.from_pandas(df, npartitions=3) actual = caller(ddf.groupby(["A", ddf["A"].eq("a1")])["B"]) assert_eq(expected, actual) @pytest.mark.skipif(DASK_EXPR_ENABLED, reason="FIXME hangs") def test_groupby_iter_fails(): df = pd.DataFrame( data=[ ["a0", "b1"], ["a1", "b1"], ["a3", "b3"], ["a5", "b5"], ], columns=["A", "B"], ) ddf = dd.from_pandas(df, npartitions=1) with pytest.raises(NotImplementedError, match="computing the groups"): list(ddf.groupby("A")) @pytest.mark.skipif(DASK_EXPR_ENABLED, reason="will raise") def test_groupby_None_split_out_warns(): df = pd.DataFrame({"a": [1, 1, 2], "b": [2, 3, 4]}) ddf = dd.from_pandas(df, npartitions=1) with pytest.warns(FutureWarning, match="split_out=None"): ddf.groupby("a").agg({"b": "max"}, split_out=None) @pytest.mark.parametrize("by", ["key1", ["key1", "key2"]]) @pytest.mark.parametrize( "slice_key", [ 3, "value", ["value"], ("value",), pd.Index(["value"]), pd.Series(["value"]), ], ) def test_groupby_slice_getitem(by, slice_key): pdf = pd.DataFrame( { "key1": ["a", "b", "a"], "key2": ["c", "c", "c"], "value": [1, 2, 3], 3: [1, 2, 3], } ) ddf = dd.from_pandas(pdf, npartitions=3) expect = pdf.groupby(by)[slice_key].count() got = ddf.groupby(by)[slice_key].count() # We should have a getitem layer, enabling # column projection after read_parquet etc if not DASK_EXPR_ENABLED: assert hlg_layer(got.dask, "getitem") assert_eq(expect, got) @pytest.mark.parametrize( "func", [ "min", "max", "sum", "prod", "first", "last", "median", pytest.param( "idxmax", marks=pytest.mark.skip(reason="https://github.com/dask/dask/issues/9882"), ), pytest.param( "idxmin", marks=pytest.mark.skip(reason="https://github.com/dask/dask/issues/9882"), ), ], ) @pytest.mark.parametrize( "numeric_only", [None, True, False], ) def test_groupby_numeric_only_supported(func, numeric_only): pdf = pd.DataFrame( { "ints": [4, 4, 5, 5, 5], "ints2": [1, 2, 3, 4, 1], "dates": pd.date_range("2015-01-01", periods=5, freq="1min"), "strings": ["q", "c", "k", "a", "l"], } ) ddf = dd.from_pandas(pdf, npartitions=3) kwargs = {} if numeric_only is None else {"numeric_only": numeric_only} ctx = contextlib.nullcontext() try: expected = getattr(pdf.groupby("ints"), func)(**kwargs) successful_compute = True except TypeError: # Make sure dask and pandas raise the same error message # We raise the error on _meta_nonempty, actual element may differ ctx = pytest.raises( TypeError, match="Cannot convert|could not convert|does not support|agg function failed", ) successful_compute = False # Here's where we check that dask behaves the same as pandas with ctx: result = getattr(ddf.groupby("ints"), func)(**kwargs) if successful_compute: # expected is None if an error was raised assert_eq(expected, result) @pytest.mark.parametrize("func", NUMERIC_ONLY_NOT_IMPLEMENTED) @pytest.mark.parametrize("numeric_only", [False, None]) def test_groupby_numeric_only_not_implemented(func, numeric_only): """These should warn / error when numeric_only is set to its default / False""" df = pd.DataFrame({"A": [1, 1, 2], "B": [3, 4, 3], "C": ["a", "b", "c"]}) ddf = dd.from_pandas(df, npartitions=3) ctx = pytest.raises( NotImplementedError, match="'numeric_only=False' is not implemented in Dask" ) # Here `numeric_only=None` means "use default value for `numeric_only`" kwargs = {} if numeric_only is None else {"numeric_only": numeric_only} with ctx: getattr(ddf.groupby("A"), func)(**kwargs) @pytest.mark.skipif( WINDOWS and sys.version_info < (3, 11), reason="https://github.com/dask/dask/pull/11320#issuecomment-2293798597", ) @pytest.mark.parametrize( "func", [ "min", "max", "sum", "prod", "first", "last", "corr", "cov", "cumprod", "cumsum", "mean", "median", "std", "var", ], ) def test_groupby_numeric_only_true(func): df = pd.DataFrame({"A": [1, 1, 2, 2], "B": [3, 4, 3, 4], "C": ["a", "b", "c", "d"]}) ddf = dd.from_pandas(df, npartitions=2) ddf_result = getattr(ddf.groupby("A"), func)(numeric_only=True) pdf_result = getattr(df.groupby("A"), func)(numeric_only=True) assert_eq(ddf_result, pdf_result) @pytest.mark.parametrize("func", ["cov", "corr"]) def test_groupby_numeric_only_false_cov_corr(func): df = pd.DataFrame( { "float": [1.0, 2.0, 3.0, 4.0, 5, 6.0, 7.0, 8.0], "int": [1, 2, 3, 4, 5, 6, 7, 8], "timedelta": pd.to_timedelta([1, 2, 3, 4, 5, 6, 7, 8]), "A": 1, } ) ddf = dd.from_pandas(df, npartitions=2) dd_result = getattr(ddf.groupby("A"), func)(numeric_only=False) pd_result = getattr(df.groupby("A"), func)(numeric_only=False) assert_eq(dd_result, pd_result) dd_result = getattr(ddf.groupby("A"), func)(numeric_only=True) pd_result = getattr(df.groupby("A"), func)(numeric_only=True) assert_eq(dd_result, pd_result) @pytest.mark.parametrize("func", ["cumsum", "cumprod"]) def test_groupby_numeric_only_false(func): df = pd.DataFrame( { "int": [1, 2, 3, 4, 5, 6, 7, 8], "float": [1.0, 2.0, 3.0, 4.0, np.nan, 6.0, 7.0, 8.0], "dt": [pd.NaT] + [datetime(2010, i, 1) for i in range(1, 8)], "A": 1, } ) ddf = dd.from_pandas(df, npartitions=2) ctx = pytest.raises(TypeError, match="does not support") with ctx: getattr(ddf.groupby("A"), func)(numeric_only=False) with ctx: getattr(df.groupby("A"), func)(numeric_only=False) with ctx: getattr(ddf.groupby("A"), func)() with ctx: getattr(df.groupby("A"), func)() @pytest.mark.parametrize("func", ["var", "std"]) @pytest.mark.parametrize("observed", [True, False]) @pytest.mark.parametrize("dropna", [True, False]) def test_groupby_var_dropna_observed(dropna, observed, func): df = pd.DataFrame( { "a": [11, 12, 31, 1, 2, 3, 4, 5, 6, 10], "b": pd.Categorical(values=[1] * 9 + [np.nan], categories=[1, 2]), } ) ddf = dd.from_pandas(df, npartitions=3) dd_result = getattr(ddf.groupby("b", observed=observed, dropna=dropna), func)() pdf_result = getattr(df.groupby("b", observed=observed, dropna=dropna), func)() assert_eq(dd_result, pdf_result) @pytest.mark.skipif(DASK_EXPR_ENABLED, reason="no deprecation") @pytest.mark.parametrize( "method", ( "sum", "prod", "min", "max", "idxmin", "idxmax", "count", "mean", "median", "size", "first", "last", "aggregate", "agg", "value_counts", "tail", "head", ), ) def test_parameter_shuffle_renamed_to_shuffle_method_deprecation(method): df = pd.DataFrame( { "a": np.random.randint(0, 10, size=100), "b": np.random.randint(0, 20, size=100), } ) ddf = dd.from_pandas(df, npartitions=2) args = () if method.startswith("agg"): args = ("sum",) group_obj = ddf.groupby("a") if method in ("value_counts", "tail", "head"): # SeriesGroupBy deprecated methods group_obj = group_obj.b msg = "the 'shuffle' keyword is deprecated, use 'shuffle_method' instead." with pytest.warns(FutureWarning, match=msg): getattr(group_obj, method)(*args, shuffle="tasks") def test_groupby_value_counts_all_na_partitions(): size = 100 na_size = 90 npartitions = 10 df = pd.DataFrame( { "A": np.random.randint(0, 2, size=size, dtype=bool), "B": np.append(np.nan * np.zeros(na_size), np.random.randn(size - na_size)), } ) ddf = dd.from_pandas(df, npartitions=npartitions) assert_eq( ddf.groupby("A")["B"].value_counts(), df.groupby("A")["B"].value_counts(), ) def test_agg_pyarrow_casts(): pytest.importorskip("pyarrow") df = pd.DataFrame( { "x": range(15), "y": pd.Series([pd.NA] * 10 + [1.0] * 5, dtype="double[pyarrow]"), "group": ["a"] * 5 + ["b"] * 5 + ["c"] * 5, } ) ddf = dd.from_pandas(df, npartitions=2) if PANDAS_GE_220: # np.sqrt doesn't work before 2.2 additional_aggs = {"z": ("y", "std")} else: additional_aggs = {} result = ddf.groupby("group").agg( x=("y", "var"), y=("y", "mean"), **additional_aggs ) expected = df.groupby("group").agg( x=("y", "var"), y=("y", "mean"), **additional_aggs ) assert_eq(result, expected)