import collections import warnings import numpy as np import pandas as pd import pandas.util.testing as tm import pytest import dask import dask.dataframe as dd from dask.dataframe.utils import ( assert_eq, assert_dask_graph, assert_max_deps, PANDAS_VERSION, ) AGG_FUNCS = ['sum', 'mean', 'min', 'max', 'count', 'size', 'std', 'var', 'nunique', 'first', 'last', 'prod'] @pytest.fixture(params=AGG_FUNCS) def agg_func(request): """ Aggregations supported for groups """ return request.param def groupby_internal_repr(): pdf = pd.DataFrame({'x': [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) # slicing should not affect to internal assert isinstance(dp.obj, dd.Series) assert_eq(dp.obj, gp.obj) 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) # slicing should not affect to internal 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, 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 groupby_error(): pdf = pd.DataFrame({'x': [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) with pytest.raises(KeyError) as err: dp[['x', 'A']] assert msg in str(err.value) def groupby_internal_head(): pdf = pd.DataFrame({'A': [1, 2] * 10, 'B': np.random.randn(20), 'C': np.random.randn(20)}) ddf = dd.from_pandas(pdf, 3) assert_eq(ddf.groupby('A')._head().sum(), pdf.head().groupby('A').sum()) assert_eq(ddf.groupby(ddf['A'])._head().sum(), pdf.head().groupby(pdf['A']).sum()) assert_eq(ddf.groupby(ddf['A'] + 1)._head().sum(), pdf.head().groupby(pdf['A'] + 1).sum()) 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()) with warnings.catch_warnings(): warnings.simplefilter('ignore') assert ddf.groupby('a').apply(func)._name.startswith('func') assert_eq(df.groupby('a').apply(func), ddf.groupby('a').apply(func)) 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)() meta = df.groupby('a').apply(func, c) with warnings.catch_warnings(): warnings.simplefilter('ignore') assert_eq(df.groupby('a').apply(func, c, d=d), ddf.groupby('a').apply(func, c, d=d_scalar)) assert_eq(df.groupby('a').apply(func, c), ddf.groupby('a').apply(func, c)) assert_eq(df.groupby('a').apply(func, c, d=d), ddf.groupby('a').apply(func, c, d=d)) assert_eq(df.groupby('a').apply(func, c), ddf.groupby('a').apply(func, c_scalar), check_dtype=False) assert_eq(df.groupby('a').apply(func, c), ddf.groupby('a').apply(func, c_scalar, meta=meta)) assert_eq(df.groupby('a').apply(func, c, d=d), ddf.groupby('a').apply(func, c, d=d_scalar, meta=meta)) # Delayed arguments work, but only if metadata is provided with pytest.raises(ValueError) as exc: ddf.groupby('a').apply(func, c, d=d_delayed) 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) assert 'dask.delayed' in str(exc.value) and 'meta'in str(exc.value) assert_eq(df.groupby('a').apply(func, c), ddf.groupby('a').apply(func, c_delayed, meta=meta)) assert_eq(df.groupby('a').apply(func, c, d=d), ddf.groupby('a').apply(func, c, d=d_delayed, meta=meta)) @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( 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.b - df.b.mean()) # last one causes a DeprcationWarning from pandas. # See https://github.com/pandas-dev/pandas/issues/16481 with warnings.catch_warnings(): warnings.simplefilter('ignore') assert_eq(df.groupby(grouper(df)).apply(func), ddf.groupby(grouper(ddf)).apply(func)) 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()) 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 pytest.warns(None): assert_eq(ddf.groupby('a').apply(func), pdf.groupby('a').apply(func)) assert_eq(ddf.groupby('a').apply(func).set_index('a'), pdf.groupby('a').apply(func).set_index('a')) assert_eq(pdf2.groupby(pdf2.index).apply(func2), ddf2.groupby(ddf2.index).apply(func2)) assert_eq(ddf2.b.groupby('a').apply(func3), pdf2.b.groupby('a').apply(func3)) assert_eq(ddf2.b.groupby(ddf2.index).apply(func3), pdf2.b.groupby(pdf2.index).apply(func3)) @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) dask_agg = getattr(dask_group, agg_func) pandas_agg = getattr(pandas_group, agg_func) 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: assert_eq(dask_agg(), pandas_agg()) 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) def test_groupby_get_group(): 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])} meta = dsk[('x', 0)] d = dd.DataFrame(dsk, 'x', meta, [0, 4, 9, 9]) full = d.compute() for ddkey, pdkey in [('b', 'b'), (d.b, full.b), (d.b + 1, full.b + 1)]: ddgrouped = d.groupby(ddkey) pdgrouped = full.groupby(pdkey) # 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() assert_eq(s.groupby('strings')['data'].nunique(), 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() assert_eq(s.groupby('strings')['data'].nunique(), 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) expected = df.groupby('x').apply(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): 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 = tm.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 = tm.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.], 'b': [1.]}, 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()) 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.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.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']]) # 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.first(), 'series-groupby-first') assert_dask_graph(ddf.groupby('b').a.last(), 'series-groupby-last') 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): 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 for m in AGG_FUNCS: # nunique is not implemented for DataFrameGroupBy if m == 'nunique': continue res = call(ddf.groupby('b'), m, **{keyword: 2}) sol = call(pdf.groupby('b'), m) assert_eq(res, sol) assert call(ddf.groupby('b'), m)._name != res._name res = call(ddf.groupby('b'), '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 for m in AGG_FUNCS: res = call(ddf.groupby('b').a, m, **{keyword: 2}) sol = call(pdf.groupby('b').a, m) assert_eq(res, sol) assert call(ddf.groupby('b').a, m)._name != res._name res = call(ddf.groupby('b').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 for m in AGG_FUNCS: res = call(ddf.a.groupby(ddf.b), m, **{keyword: 2}) sol = call(pdf.a.groupby(pdf.b), m) # 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), m)._name != res._name res = call(ddf.a.groupby(ddf.b), '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 def test_apply_shuffle(): 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(ddf.groupby('A').apply(lambda x: x.sum()), pdf.groupby('A').apply(lambda x: x.sum())) assert_eq(ddf.groupby(ddf['A']).apply(lambda x: x.sum()), pdf.groupby(pdf['A']).apply(lambda x: x.sum())) assert_eq(ddf.groupby(ddf['A'] + 1).apply(lambda x: x.sum()), pdf.groupby(pdf['A'] + 1).apply(lambda x: x.sum())) # SeriesGroupBy assert_eq(ddf.groupby('A')['B'].apply(lambda x: x.sum()), pdf.groupby('A')['B'].apply(lambda x: x.sum())) assert_eq(ddf.groupby(ddf['A'])['B'].apply(lambda x: x.sum()), pdf.groupby(pdf['A'])['B'].apply(lambda x: x.sum())) assert_eq(ddf.groupby(ddf['A'] + 1)['B'].apply(lambda x: x.sum()), pdf.groupby(pdf['A'] + 1)['B'].apply(lambda x: x.sum())) # Series.groupby assert_eq(ddf.B.groupby(ddf['A']).apply(lambda x: x.sum()), pdf.B.groupby(pdf['A']).apply(lambda x: x.sum())) assert_eq(ddf.B.groupby(ddf['A'] + 1).apply(lambda x: x.sum()), pdf.B.groupby(pdf['A'] + 1).apply(lambda x: x.sum())) # DataFrameGroupBy with column slice assert_eq(ddf.groupby('A')[['B', 'C']].apply(lambda x: x.sum()), pdf.groupby('A')[['B', 'C']].apply(lambda x: x.sum())) assert_eq(ddf.groupby(ddf['A'])[['B', 'C']].apply(lambda x: x.sum()), pdf.groupby(pdf['A'])[['B', 'C']].apply(lambda x: x.sum())) assert_eq(ddf.groupby(ddf['A'] + 1)[['B', 'C']].apply(lambda x: x.sum()), pdf.groupby(pdf['A'] + 1)[['B', 'C']].apply(lambda x: x.sum())) @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("NotImplemented")) ]) def test_apply_shuffle_multilevel(grouper): 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(ddf.groupby(grouper(ddf)).apply(lambda x: x.sum()), pdf.groupby(grouper(pdf)).apply(lambda x: x.sum())) # SeriesGroupBy assert_eq(ddf.groupby(grouper(ddf))['B'].apply(lambda x: x.sum()), pdf.groupby(grouper(pdf))['B'].apply(lambda x: x.sum())) # DataFrameGroupBy with column slice assert_eq(ddf.groupby(grouper(ddf))[['B', 'C']].apply(lambda x: x.sum()), pdf.groupby(grouper(pdf))[['B', 'C']].apply(lambda x: x.sum())) 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()) assert_eq(ddf.groupby(0).apply(lambda x: x, meta={0: int, 1: int, 2: int}), df.groupby(0).apply(lambda x: x)) def test_groupby_apply_tasks(): df = pd.util.testing.makeTimeDataFrame() df['A'] = df.A // 0.1 df['B'] = df.B // 0.1 ddf = dd.from_pandas(df, npartitions=10) with dask.config.set(shuffle='tasks'): for ind in [lambda x: 'A', lambda x: x.A]: a = df.groupby(ind(df)).apply(len) with pytest.warns(UserWarning): b = ddf.groupby(ind(ddf)).apply(len) 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) with pytest.warns(UserWarning): b = ddf.groupby(ind(ddf)).B.apply(len) 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) with dask.config.set(scheduler='processes'): assert_eq(ddf.groupby('B').apply(lambda x: x, meta={"A": int, "B": object}), df.groupby('B').apply(lambda x: x)) def test_groupby_normalize_index(): 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) assert d.groupby('a').index == 'a' assert d.groupby(d['a']).index == 'a' assert d.groupby(d['a'] > 2).index._name == (d['a'] > 2)._name assert d.groupby(['a', 'b']).index == ['a', 'b'] assert d.groupby([d['a'], d['b']]).index == ['a', 'b'] assert d.groupby([d['a'], 'b']).index == ['a', 'b'] @pytest.mark.parametrize('spec', [ {'b': {'c': 'mean'}, 'c': {'a': 'max', 'b': 'min'}}, {'b': 'mean', 'c': ['min', 'max']}, {'b': np.sum, 'c': ['min', np.max, np.std, np.var]}, ['sum', 'mean', 'min', 'max', 'count', 'size', 'std', 'var', 'first', 'last', 'prod'], 'var', {'b': 'mean', 'c': 'first', 'd': 'last', 'a': ['first', 'last']}, {'b': {'c': 'mean'}, 'c': {'a': 'first', 'b': 'last'}}, ]) @pytest.mark.parametrize('split_every', [False, None]) @pytest.mark.parametrize('grouper', [ lambda df: 'a', lambda df: ['a', 'd'], lambda df: [df['a'], df['d']], lambda df: df['a'], lambda df: df['a'] > 2, ]) def test_aggregate__examples(spec, split_every, grouper): 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) # Warning from pandas deprecation .agg(dict[dict]) # it's from pandas, so no reason to assert the deprecation warning, # but we should still test it for now with pytest.warns(None): assert_eq(pdf.groupby(grouper(pdf)).agg(spec), ddf.groupby(grouper(ddf)).agg(spec, split_every=split_every)) @pytest.mark.parametrize('spec', [ {'b': 'sum', 'c': 'min', 'd': 'max'}, ['sum'], ['sum', 'mean', 'min', 'max', 'count', 'size', 'std', 'var', 'first', 'last'], 'sum', 'size', ]) @pytest.mark.parametrize('split_every', [False, None]) @pytest.mark.parametrize('grouper', [ lambda df: [df['a'], df['d']], lambda df: df['a'], lambda df: df['a'] > 2, ]) def test_series_aggregate__examples(spec, split_every, grouper): 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']) ps = pdf['c'] ddf = dd.from_pandas(pdf, npartitions=10) ds = ddf['c'] # Warning from pandas deprecation .agg(dict[dict]) # it's from pandas, so no reason to assert the deprecation warning, # but we should still test it for now with pytest.warns(None): assert_eq(ps.groupby(grouper(pdf)).agg(spec), ds.groupby(grouper(ddf)).agg(spec, split_every=split_every)) def test_aggregate__single_element_groups(agg_func): spec = agg_func # nunique is not supported in specs if spec == 'nunique': 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 ot the original dtype if spec in {'mean', 'var'}: expected = expected.astype(float) assert_eq(expected, ddf.groupby(['a', 'd']).agg(spec)) 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 accross 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) def test_aggregate__dask(): dask_holder = collections.namedtuple('dask_holder', ['dask']) get_agg_dask = lambda obj: dask_holder({ k: v for (k, v) in obj.dask.items() if k[0].startswith('aggregate') }) specs = [ {'b': {'c': 'mean'}, 'c': {'a': 'max', 'b': 'min'}}, {'b': 'mean', 'c': ['min', 'max']}, ['sum', 'mean', 'min', 'max', 'count', 'size', 'std', 'var', 'first', 'last', 'prod'], 'sum', 'mean', 'min', 'max', 'count', 'std', 'var', 'first', 'last', 'prod' # NOTE: the 'size' spec is special since it bypasses aggregate # 'size' ] 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) for spec in specs: result1 = ddf.groupby(['a', 'b']).agg(spec, split_every=2) result2 = ddf.groupby(['a', 'b']).agg(spec, split_every=2) agg_dask1 = get_agg_dask(result1) agg_dask2 = get_agg_dask(result2) # check that the number of partitions used is fixed by split_every assert_max_deps(agg_dask1, 2) assert_max_deps(agg_dask2, 2) # check for deterministic key names and values assert agg_dask1 == agg_dask2 # the length of the dask does not depend on the passed spec for other_spec in specs: other = ddf.groupby(['a', 'b']).agg(other_spec, split_every=2) assert len(other.dask) == len(result1.dask) assert len(other.dask) == len(result2.dask) @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] ]) def test_dataframe_aggregations_multilevel(grouper, agg_func): 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) assert_eq(call(pdf.groupby(grouper(pdf))['c'], agg_func), call(ddf.groupby(grouper(ddf))['c'], agg_func, split_every=2)) # not supported by pandas if agg_func != 'nunique': assert_eq(call(pdf.groupby(grouper(pdf))[['c', 'd']], agg_func), call(ddf.groupby(grouper(ddf))[['c', 'd']], agg_func, split_every=2)) assert_eq(call(pdf.groupby(grouper(pdf)), agg_func), call(ddf.groupby(grouper(ddf)), agg_func, 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] ]) def test_series_aggregations_multilevel(grouper, agg_func): """ similar to ``test_dataframe_aggregations_multilevel``, but series do not support all groupby args. """ 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, '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)), agg_func), call(ddf['c'].groupby(grouper(ddf)), agg_func, 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.parametrize('grouper', [ lambda df: df['a'], lambda df: df['a'] > 2, lambda df: [df['a'], df['b']], lambda df: [df['a'] > 2], pytest.param(lambda df: [df['a'] > 2, df['b'] > 1], marks=pytest.mark.xfail( reason="index dtype does not coincide: boolean != empty")) ]) @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', [None, 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').y.var(split_every=split_every, split_out=split_out) dsk = result.__dask_optimize__(result.dask, result.__dask_keys__()) from dask.core import get_deps dependencies, dependents = get_deps(dsk) assert result.npartitions == (split_out or 1) assert len([k for k, v in dependencies.items() if not v]) == npartitions 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']).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').sum(split_out=2).npartitions == 2 assert ddf.groupby('A').sum(split_out=3).npartitions == 3 with pytest.raises(TypeError): # groupby doesn't adcept 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', sort=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.parametrize('sel', ['c', 'd', ['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)()) @pytest.mark.parametrize('func', ['cumsum', 'cumprod']) def test_cumulative_axis1(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) assert_eq(getattr(df.groupby('a'), func)(axis=1), getattr(ddf.groupby('a'), func)(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) good = [(ddf_group.apply(add1, meta=ddf), df_group.apply(add1)), (ddf_group.b.apply(add1, meta=ddf.b), df_group.b.apply(add1))] for (res, sol) in good: assert_eq(res, sol) def test_groupby_dataframe_cum_caching(): """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) ops = ['cumsum', 'cumprod'] for op in ops: 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) 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.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: np.max(s) - np.mean(s)]) 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) # Expected result expected = df.groupby(group_args).apply(apply_func) with warnings.catch_warnings(): warnings.simplefilter('ignore') # Compute on dask DataFrame with divisions (no shuffling) result = ddf.groupby(group_args).apply(apply_func) 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) # Compute on dask DataFrame without divisions (requires shuffling) result = ddf_no_divs.groupby(group_args).apply(apply_func) 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) agg_func = dd.Aggregation( 'custom_mode', lambda s: s.apply(lambda s: [s.value_counts()]), 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) def test_groupby_select_column_agg(): 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('var') expected = pdf.groupby('A')['B'].agg('var') assert_eq(actual, expected) @pytest.mark.parametrize('func', [ lambda x: x.std(), lambda x: x.groupby('x').std(), lambda x: x.groupby('x').var(), lambda x: x.groupby('x').mean(), lambda x: x.groupby('x').sum(), lambda x: x.groupby('x').z.std(), ]) def test_std_object_dtype(func): df = pd.DataFrame({ 'x': [1, 2, 1], 'y': ['a', 'b', 'c'], 'z': [11., 22., 33.], }) ddf = dd.from_pandas(df, npartitions=2) assert_eq(func(df), func(ddf)) def test_timeseries(): df = dask.datasets.timeseries().partitions[:2] assert_eq(df.groupby('name').std(), df.groupby('name').std()) @pytest.mark.skipif(PANDAS_VERSION < '0.22.0', reason="Parameter min_count not implemented in " "DataFrame.groupby().sum() and DataFrame.groupby().prod()") @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)) def test_groupby_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) assert_eq(expected, ddf.groupby('a').apply(func, meta=expected)) expected = pdf.groupby('a', group_keys=False).apply(func) assert_eq(expected, ddf.groupby('a', group_keys=False).apply(func, meta=expected))