from datetime import datetime import warnings import pytest import numpy as np import pandas as pd import dask.dataframe as dd from dask.dataframe.utils import ( assert_eq, assert_dask_graph, make_meta, HAS_INT_NA, ) @pytest.mark.slow def test_arithmetics(): dsk = {('x', 0): pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}, index=[0, 1, 3]), ('x', 1): pd.DataFrame({'a': [4, 5, 6], 'b': [3, 2, 1]}, index=[5, 6, 8]), ('x', 2): pd.DataFrame({'a': [7, 8, 9], 'b': [0, 0, 0]}, index=[9, 9, 9])} meta = make_meta({'a': 'i8', 'b': 'i8'}, index=pd.Index([], 'i8')) ddf1 = dd.DataFrame(dsk, 'x', meta, [0, 4, 9, 9]) pdf1 = ddf1.compute() pdf2 = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6, 7, 8], 'b': [5, 6, 7, 8, 1, 2, 3, 4]}) pdf3 = pd.DataFrame({'a': [5, 6, 7, 8, 4, 3, 2, 1], 'b': [2, 4, 5, 3, 4, 2, 1, 0]}) ddf2 = dd.from_pandas(pdf2, 3) ddf3 = dd.from_pandas(pdf3, 2) dsk4 = {('y', 0): pd.DataFrame({'a': [3, 2, 1], 'b': [7, 8, 9]}, index=[0, 1, 3]), ('y', 1): pd.DataFrame({'a': [5, 2, 8], 'b': [4, 2, 3]}, index=[5, 6, 8]), ('y', 2): pd.DataFrame({'a': [1, 4, 10], 'b': [1, 0, 5]}, index=[9, 9, 9])} ddf4 = dd.DataFrame(dsk4, 'y', meta, [0, 4, 9, 9]) pdf4 = ddf4.compute() # Arithmetics cases = [(ddf1, ddf1, pdf1, pdf1), (ddf1, ddf1.repartition([0, 1, 3, 6, 9]), pdf1, pdf1), (ddf2, ddf3, pdf2, pdf3), (ddf2.repartition([0, 3, 6, 7]), ddf3.repartition([0, 7]), pdf2, pdf3), (ddf2.repartition([0, 7]), ddf3.repartition([0, 2, 4, 5, 7]), pdf2, pdf3), (ddf1, ddf4, pdf1, pdf4), (ddf1, ddf4.repartition([0, 9]), pdf1, pdf4), (ddf1.repartition([0, 3, 9]), ddf4.repartition([0, 5, 9]), pdf1, pdf4), # dask + pandas (ddf1, pdf4, pdf1, pdf4), (ddf2, pdf3, pdf2, pdf3)] for (l, r, el, er) in cases: check_series_arithmetics(l.a, r.b, el.a, er.b) check_frame_arithmetics(l, r, el, er) # different index, pandas raises ValueError in comparison ops pdf5 = pd.DataFrame({'a': [3, 2, 1, 5, 2, 8, 1, 4, 10], 'b': [7, 8, 9, 4, 2, 3, 1, 0, 5]}, index=[0, 1, 3, 5, 6, 8, 9, 9, 9]) ddf5 = dd.from_pandas(pdf5, 2) pdf6 = pd.DataFrame({'a': [3, 2, 1, 5, 2, 8, 1, 4, 10], 'b': [7, 8, 9, 5, 7, 8, 4, 2, 5]}, index=[0, 1, 2, 3, 4, 5, 6, 7, 9]) ddf6 = dd.from_pandas(pdf6, 4) pdf7 = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6, 7, 8], 'b': [5, 6, 7, 8, 1, 2, 3, 4]}, index=list('aaabcdeh')) pdf8 = pd.DataFrame({'a': [5, 6, 7, 8, 4, 3, 2, 1], 'b': [2, 4, 5, 3, 4, 2, 1, 0]}, index=list('abcdefgh')) ddf7 = dd.from_pandas(pdf7, 3) ddf8 = dd.from_pandas(pdf8, 4) pdf9 = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6, 7, 8], 'b': [5, 6, 7, 8, 1, 2, 3, 4], 'c': [5, 6, 7, 8, 1, 2, 3, 4]}, index=list('aaabcdeh')) pdf10 = pd.DataFrame({'b': [5, 6, 7, 8, 4, 3, 2, 1], 'c': [2, 4, 5, 3, 4, 2, 1, 0], 'd': [2, 4, 5, 3, 4, 2, 1, 0]}, index=list('abcdefgh')) ddf9 = dd.from_pandas(pdf9, 3) ddf10 = dd.from_pandas(pdf10, 4) # Arithmetics with different index cases = [(ddf5, ddf6, pdf5, pdf6), (ddf5.repartition([0, 9]), ddf6, pdf5, pdf6), (ddf5.repartition([0, 5, 9]), ddf6.repartition([0, 7, 9]), pdf5, pdf6), (ddf7, ddf8, pdf7, pdf8), (ddf7.repartition(['a', 'c', 'h']), ddf8.repartition(['a', 'h']), pdf7, pdf8), (ddf7.repartition(['a', 'b', 'e', 'h']), ddf8.repartition(['a', 'e', 'h']), pdf7, pdf8), (ddf9, ddf10, pdf9, pdf10), (ddf9.repartition(['a', 'c', 'h']), ddf10.repartition(['a', 'h']), pdf9, pdf10), # dask + pandas (ddf5, pdf6, pdf5, pdf6), (ddf7, pdf8, pdf7, pdf8), (ddf9, pdf10, pdf9, pdf10)] for (l, r, el, er) in cases: check_series_arithmetics(l.a, r.b, el.a, er.b, allow_comparison_ops=False) check_frame_arithmetics(l, r, el, er, allow_comparison_ops=False) def test_deterministic_arithmetic_names(): df = pd.DataFrame({'x': [1, 2, 3, 4], 'y': [5, 6, 7, 8]}) a = dd.from_pandas(df, npartitions=2) assert sorted((a.x + a.y ** 2).dask) == sorted((a.x + a.y ** 2).dask) assert sorted((a.x + a.y ** 2).dask) != sorted((a.x + a.y ** 3).dask) assert sorted((a.x + a.y ** 2).dask) != sorted((a.x - a.y ** 2).dask) @pytest.mark.slow def test_arithmetics_different_index(): # index are different, but overwraps pdf1 = pd.DataFrame({'a': [1, 2, 3, 4, 5], 'b': [3, 5, 2, 5, 7]}, index=[1, 2, 3, 4, 5]) ddf1 = dd.from_pandas(pdf1, 2) pdf2 = pd.DataFrame({'a': [3, 2, 6, 7, 8], 'b': [9, 4, 2, 6, 2]}, index=[3, 4, 5, 6, 7]) ddf2 = dd.from_pandas(pdf2, 2) # index are not overwrapped pdf3 = pd.DataFrame({'a': [1, 2, 3, 4, 5], 'b': [3, 5, 2, 5, 7]}, index=[1, 2, 3, 4, 5]) ddf3 = dd.from_pandas(pdf3, 2) pdf4 = pd.DataFrame({'a': [3, 2, 6, 7, 8], 'b': [9, 4, 2, 6, 2]}, index=[10, 11, 12, 13, 14]) ddf4 = dd.from_pandas(pdf4, 2) # index is included in another pdf5 = pd.DataFrame({'a': [1, 2, 3, 4, 5], 'b': [3, 5, 2, 5, 7]}, index=[1, 3, 5, 7, 9]) ddf5 = dd.from_pandas(pdf5, 2) pdf6 = pd.DataFrame({'a': [3, 2, 6, 7, 8], 'b': [9, 4, 2, 6, 2]}, index=[2, 3, 4, 5, 6]) ddf6 = dd.from_pandas(pdf6, 2) cases = [(ddf1, ddf2, pdf1, pdf2), (ddf2, ddf1, pdf2, pdf1), (ddf1.repartition([1, 3, 5]), ddf2.repartition([3, 4, 7]), pdf1, pdf2), (ddf2.repartition([3, 4, 5, 7]), ddf1.repartition([1, 2, 4, 5]), pdf2, pdf1), (ddf3, ddf4, pdf3, pdf4), (ddf4, ddf3, pdf4, pdf3), (ddf3.repartition([1, 2, 3, 4, 5]), ddf4.repartition([10, 11, 12, 13, 14]), pdf3, pdf4), (ddf4.repartition([10, 14]), ddf3.repartition([1, 3, 4, 5]), pdf4, pdf3), (ddf5, ddf6, pdf5, pdf6), (ddf6, ddf5, pdf6, pdf5), (ddf5.repartition([1, 7, 8, 9]), ddf6.repartition([2, 3, 4, 6]), pdf5, pdf6), (ddf6.repartition([2, 6]), ddf5.repartition([1, 3, 7, 9]), pdf6, pdf5), # dask + pandas (ddf1, pdf2, pdf1, pdf2), (ddf2, pdf1, pdf2, pdf1), (ddf3, pdf4, pdf3, pdf4), (ddf4, pdf3, pdf4, pdf3), (ddf5, pdf6, pdf5, pdf6), (ddf6, pdf5, pdf6, pdf5)] for (l, r, el, er) in cases: check_series_arithmetics(l.a, r.b, el.a, er.b, allow_comparison_ops=False) check_frame_arithmetics(l, r, el, er, allow_comparison_ops=False) pdf7 = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6, 7, 8], 'b': [5, 6, 7, 8, 1, 2, 3, 4]}, index=[0, 2, 4, 8, 9, 10, 11, 13]) pdf8 = pd.DataFrame({'a': [5, 6, 7, 8, 4, 3, 2, 1], 'b': [2, 4, 5, 3, 4, 2, 1, 0]}, index=[1, 3, 4, 8, 9, 11, 12, 13]) ddf7 = dd.from_pandas(pdf7, 3) ddf8 = dd.from_pandas(pdf8, 2) pdf9 = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6, 7, 8], 'b': [5, 6, 7, 8, 1, 2, 3, 4]}, index=[0, 2, 4, 8, 9, 10, 11, 13]) pdf10 = pd.DataFrame({'a': [5, 6, 7, 8, 4, 3, 2, 1], 'b': [2, 4, 5, 3, 4, 2, 1, 0]}, index=[0, 3, 4, 8, 9, 11, 12, 13]) ddf9 = dd.from_pandas(pdf9, 3) ddf10 = dd.from_pandas(pdf10, 2) cases = [(ddf7, ddf8, pdf7, pdf8), (ddf8, ddf7, pdf8, pdf7), # (ddf7.repartition([0, 13]), # ddf8.repartition([0, 4, 11, 14], force=True), # pdf7, pdf8), (ddf8.repartition([-5, 10, 15], force=True), ddf7.repartition([-1, 4, 11, 14], force=True), pdf8, pdf7), (ddf7.repartition([0, 8, 12, 13]), ddf8.repartition([0, 2, 8, 12, 13], force=True), pdf7, pdf8), (ddf8.repartition([-5, 0, 10, 20], force=True), ddf7.repartition([-1, 4, 11, 13], force=True), pdf8, pdf7), (ddf9, ddf10, pdf9, pdf10), (ddf10, ddf9, pdf10, pdf9), # dask + pandas (ddf7, pdf8, pdf7, pdf8), (ddf8, pdf7, pdf8, pdf7), (ddf9, pdf10, pdf9, pdf10), (ddf10, pdf9, pdf10, pdf9)] for (l, r, el, er) in cases: check_series_arithmetics(l.a, r.b, el.a, er.b, allow_comparison_ops=False) check_frame_arithmetics(l, r, el, er, allow_comparison_ops=False) def check_series_arithmetics(l, r, el, er, allow_comparison_ops=True): assert isinstance(l, dd.Series) assert isinstance(r, (dd.Series, pd.Series)) assert isinstance(el, pd.Series) assert isinstance(er, pd.Series) # l, r may be repartitioned, test whether repartition keeps original data assert_eq(l, el) assert_eq(r, er) assert_eq(l + r, el + er) assert_eq(l * r, el * er) assert_eq(l - r, el - er) assert_eq(l / r, el / er) assert_eq(l // r, el // er) assert_eq(l ** r, el ** er) assert_eq(l % r, el % er) if allow_comparison_ops: # comparison is allowed if data have same index assert_eq(l & r, el & er) assert_eq(l | r, el | er) assert_eq(l ^ r, el ^ er) assert_eq(l > r, el > er) assert_eq(l < r, el < er) assert_eq(l >= r, el >= er) assert_eq(l <= r, el <= er) assert_eq(l == r, el == er) assert_eq(l != r, el != er) assert_eq(l.lt(r), el.lt(er)) assert_eq(l.gt(r), el.gt(er)) assert_eq(l.le(r), el.le(er)) assert_eq(l.ge(r), el.ge(er)) assert_eq(l.ne(r), el.ne(er)) assert_eq(l.eq(r), el.eq(er)) assert_eq(l + 2, el + 2) assert_eq(l * 2, el * 2) assert_eq(l - 2, el - 2) assert_eq(l / 2, el / 2) assert_eq(l & True, el & True) assert_eq(l | True, el | True) assert_eq(l ^ True, el ^ True) assert_eq(l // 2, el // 2) assert_eq(l ** 2, el ** 2) assert_eq(l % 2, el % 2) assert_eq(l > 2, el > 2) assert_eq(l < 2, el < 2) assert_eq(l >= 2, el >= 2) assert_eq(l <= 2, el <= 2) assert_eq(l == 2, el == 2) assert_eq(l != 2, el != 2) assert_eq(2 + r, 2 + er) assert_eq(2 * r, 2 * er) assert_eq(2 - r, 2 - er) assert_eq(2 / r, 2 / er) assert_eq(True & r, True & er) assert_eq(True | r, True | er) assert_eq(True ^ r, True ^ er) assert_eq(2 // r, 2 // er) assert_eq(2 ** r, 2 ** er) assert_eq(2 % r, 2 % er) assert_eq(2 > r, 2 > er) assert_eq(2 < r, 2 < er) assert_eq(2 >= r, 2 >= er) assert_eq(2 <= r, 2 <= er) assert_eq(2 == r, 2 == er) assert_eq(2 != r, 2 != er) assert_eq(l.lt(2), el.lt(2)) assert_eq(l.gt(2), el.gt(2)) assert_eq(l.le(2), el.le(2)) assert_eq(l.ge(2), el.ge(2)) assert_eq(l.ne(2), el.ne(2)) assert_eq(l.eq(2), el.eq(2)) assert_eq(-l, -el) assert_eq(abs(l), abs(el)) if allow_comparison_ops: # comparison is allowed if data have same index assert_eq(~(l == r), ~(el == er)) def check_frame_arithmetics(l, r, el, er, allow_comparison_ops=True): assert isinstance(l, dd.DataFrame) assert isinstance(r, (dd.DataFrame, pd.DataFrame)) assert isinstance(el, pd.DataFrame) assert isinstance(er, pd.DataFrame) # l, r may be repartitioned, test whether repartition keeps original data assert_eq(l, el) assert_eq(r, er) assert_eq(l + r, el + er) assert_eq(l * r, el * er) assert_eq(l - r, el - er) assert_eq(l / r, el / er) assert_eq(l // r, el // er) assert_eq(l ** r, el ** er) assert_eq(l % r, el % er) if allow_comparison_ops: # comparison is allowed if data have same index assert_eq(l & r, el & er) assert_eq(l | r, el | er) assert_eq(l ^ r, el ^ er) assert_eq(l > r, el > er) assert_eq(l < r, el < er) assert_eq(l >= r, el >= er) assert_eq(l <= r, el <= er) assert_eq(l == r, el == er) assert_eq(l != r, el != er) assert_eq(l.lt(r), el.lt(er)) assert_eq(l.gt(r), el.gt(er)) assert_eq(l.le(r), el.le(er)) assert_eq(l.ge(r), el.ge(er)) assert_eq(l.ne(r), el.ne(er)) assert_eq(l.eq(r), el.eq(er)) assert_eq(l + 2, el + 2) assert_eq(l * 2, el * 2) assert_eq(l - 2, el - 2) assert_eq(l / 2, el / 2) assert_eq(l & True, el & True) assert_eq(l | True, el | True) assert_eq(l ^ True, el ^ True) assert_eq(l // 2, el // 2) assert_eq(l ** 2, el ** 2) assert_eq(l % 2, el % 2) assert_eq(l > 2, el > 2) assert_eq(l < 2, el < 2) assert_eq(l >= 2, el >= 2) assert_eq(l <= 2, el <= 2) assert_eq(l == 2, el == 2) assert_eq(l != 2, el != 2) assert_eq(2 + l, 2 + el) assert_eq(2 * l, 2 * el) assert_eq(2 - l, 2 - el) assert_eq(2 / l, 2 / el) assert_eq(True & l, True & el) assert_eq(True | l, True | el) assert_eq(True ^ l, True ^ el) assert_eq(2 // l, 2 // el) assert_eq(2 ** l, 2 ** el) assert_eq(2 % l, 2 % el) assert_eq(2 > l, 2 > el) assert_eq(2 < l, 2 < el) assert_eq(2 >= l, 2 >= el) assert_eq(2 <= l, 2 <= el) assert_eq(2 == l, 2 == el) assert_eq(2 != l, 2 != el) assert_eq(l.lt(2), el.lt(2)) assert_eq(l.gt(2), el.gt(2)) assert_eq(l.le(2), el.le(2)) assert_eq(l.ge(2), el.ge(2)) assert_eq(l.ne(2), el.ne(2)) assert_eq(l.eq(2), el.eq(2)) assert_eq(-l, -el) assert_eq(abs(l), abs(el)) if allow_comparison_ops: # comparison is allowed if data have same index assert_eq(~(l == r), ~(el == er)) def test_scalar_arithmetics(): el = np.int64(10) er = np.int64(4) l = dd.core.Scalar({('l', 0): el}, 'l', 'i8') r = dd.core.Scalar({('r', 0): er}, 'r', 'i8') assert isinstance(l, dd.core.Scalar) assert isinstance(r, dd.core.Scalar) assert_eq(l, el) assert_eq(r, er) assert_eq(l + r, el + er) assert_eq(l * r, el * er) assert_eq(l - r, el - er) assert_eq(l / r, el / er) assert_eq(l // r, el // er) assert_eq(l ** r, el ** er) assert_eq(l % r, el % er) assert_eq(l & r, el & er) assert_eq(l | r, el | er) assert_eq(l ^ r, el ^ er) assert_eq(l > r, el > er) assert_eq(l < r, el < er) assert_eq(l >= r, el >= er) assert_eq(l <= r, el <= er) assert_eq(l == r, el == er) assert_eq(l != r, el != er) assert_eq(l + 2, el + 2) assert_eq(l * 2, el * 2) assert_eq(l - 2, el - 2) assert_eq(l / 2, el / 2) assert_eq(l & True, el & True) assert_eq(l | True, el | True) assert_eq(l ^ True, el ^ True) assert_eq(l // 2, el // 2) assert_eq(l ** 2, el ** 2) assert_eq(l % 2, el % 2) assert_eq(l > 2, el > 2) assert_eq(l < 2, el < 2) assert_eq(l >= 2, el >= 2) assert_eq(l <= 2, el <= 2) assert_eq(l == 2, el == 2) assert_eq(l != 2, el != 2) assert_eq(2 + r, 2 + er) assert_eq(2 * r, 2 * er) assert_eq(2 - r, 2 - er) assert_eq(2 / r, 2 / er) assert_eq(True & r, True & er) assert_eq(True | r, True | er) assert_eq(True ^ r, True ^ er) assert_eq(2 // r, 2 // er) assert_eq(2 ** r, 2 ** er) assert_eq(2 % r, 2 % er) assert_eq(2 > r, 2 > er) assert_eq(2 < r, 2 < er) assert_eq(2 >= r, 2 >= er) assert_eq(2 <= r, 2 <= er) assert_eq(2 == r, 2 == er) assert_eq(2 != r, 2 != er) assert_eq(-l, -el) assert_eq(abs(l), abs(el)) assert_eq(~(l == r), ~(el == er)) def test_scalar_arithmetics_with_dask_instances(): s = dd.core.Scalar({('s', 0): 10}, 's', 'i8') e = 10 pds = pd.Series([1, 2, 3, 4, 5, 6, 7]) dds = dd.from_pandas(pds, 2) pdf = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6, 7], 'b': [7, 6, 5, 4, 3, 2, 1]}) ddf = dd.from_pandas(pdf, 2) # pandas Series result = pds + s # this result pd.Series (automatically computed) assert isinstance(result, pd.Series) assert_eq(result, pds + e) result = s + pds # this result dd.Series assert isinstance(result, dd.Series) assert_eq(result, pds + e) # dask Series result = dds + s # this result dd.Series assert isinstance(result, dd.Series) assert_eq(result, pds + e) result = s + dds # this result dd.Series assert isinstance(result, dd.Series) assert_eq(result, pds + e) # pandas DataFrame result = pdf + s # this result pd.DataFrame (automatically computed) assert isinstance(result, pd.DataFrame) assert_eq(result, pdf + e) result = s + pdf # this result dd.DataFrame assert isinstance(result, dd.DataFrame) assert_eq(result, pdf + e) # dask DataFrame result = ddf + s # this result dd.DataFrame assert isinstance(result, dd.DataFrame) assert_eq(result, pdf + e) result = s + ddf # this result dd.DataFrame assert isinstance(result, dd.DataFrame) assert_eq(result, pdf + e) def test_frame_series_arithmetic_methods(): pdf1 = pd.DataFrame({'A': np.arange(10), 'B': [np.nan, 1, 2, 3, 4] * 2, 'C': [np.nan] * 10, 'D': np.arange(10)}, index=list('abcdefghij'), columns=list('ABCD')) pdf2 = pd.DataFrame(np.random.randn(10, 4), index=list('abcdefghjk'), columns=list('ABCX')) ps1 = pdf1.A ps2 = pdf2.A ps3 = pd.Series(np.random.randn(10), index=list('ABCDXabcde')) ddf1 = dd.from_pandas(pdf1, 2) ddf2 = dd.from_pandas(pdf2, 2) ds1 = ddf1.A ds2 = ddf2.A s = dd.core.Scalar({('s', 0): 4}, 's', 'i8') for l, r, el, er in [(ddf1, ddf2, pdf1, pdf2), (ds1, ds2, ps1, ps2), (ddf1.repartition(['a', 'f', 'j']), ddf2, pdf1, pdf2), (ds1.repartition(['a', 'b', 'f', 'j']), ds2, ps1, ps2), (ddf1, ddf2.repartition(['a', 'k']), pdf1, pdf2), (ds1, ds2.repartition(['a', 'b', 'd', 'h', 'k']), ps1, ps2), (ddf1, 3, pdf1, 3), (ds1, 3, ps1, 3), (ddf1, s, pdf1, 4), (ds1, s, ps1, 4)]: # l, r may be repartitioned, test whether repartition keeps original data assert_eq(l, el) assert_eq(r, er) assert_eq(l.add(r, fill_value=0), el.add(er, fill_value=0)) assert_eq(l.sub(r, fill_value=0), el.sub(er, fill_value=0)) assert_eq(l.mul(r, fill_value=0), el.mul(er, fill_value=0)) assert_eq(l.div(r, fill_value=0), el.div(er, fill_value=0)) assert_eq(l.truediv(r, fill_value=0), el.truediv(er, fill_value=0)) assert_eq(l.floordiv(r, fill_value=1), el.floordiv(er, fill_value=1)) assert_eq(l.mod(r, fill_value=0), el.mod(er, fill_value=0)) assert_eq(l.pow(r, fill_value=0), el.pow(er, fill_value=0)) assert_eq(l.radd(r, fill_value=0), el.radd(er, fill_value=0)) assert_eq(l.rsub(r, fill_value=0), el.rsub(er, fill_value=0)) assert_eq(l.rmul(r, fill_value=0), el.rmul(er, fill_value=0)) assert_eq(l.rdiv(r, fill_value=0), el.rdiv(er, fill_value=0)) assert_eq(l.rtruediv(r, fill_value=0), el.rtruediv(er, fill_value=0)) assert_eq(l.rfloordiv(r, fill_value=1), el.rfloordiv(er, fill_value=1)) assert_eq(l.rmod(r, fill_value=0), el.rmod(er, fill_value=0)) assert_eq(l.rpow(r, fill_value=0), el.rpow(er, fill_value=0)) for l, r, el, er in [(ddf1, ds2, pdf1, ps2), (ddf1, ddf2.X, pdf1, pdf2.X)]: assert_eq(l, el) assert_eq(r, er) # must specify axis=0 to add Series to each column # axis=1 is not supported (add to each row) assert_eq(l.add(r, axis=0), el.add(er, axis=0)) assert_eq(l.sub(r, axis=0), el.sub(er, axis=0)) assert_eq(l.mul(r, axis=0), el.mul(er, axis=0)) assert_eq(l.div(r, axis=0), el.div(er, axis=0)) assert_eq(l.truediv(r, axis=0), el.truediv(er, axis=0)) assert_eq(l.floordiv(r, axis=0), el.floordiv(er, axis=0)) assert_eq(l.mod(r, axis=0), el.mod(er, axis=0)) assert_eq(l.pow(r, axis=0), el.pow(er, axis=0)) assert_eq(l.radd(r, axis=0), el.radd(er, axis=0)) assert_eq(l.rsub(r, axis=0), el.rsub(er, axis=0)) assert_eq(l.rmul(r, axis=0), el.rmul(er, axis=0)) assert_eq(l.rdiv(r, axis=0), el.rdiv(er, axis=0)) assert_eq(l.rtruediv(r, axis=0), el.rtruediv(er, axis=0)) assert_eq(l.rfloordiv(r, axis=0), el.rfloordiv(er, axis=0)) assert_eq(l.rmod(r, axis=0), el.rmod(er, axis=0)) assert_eq(l.rpow(r, axis=0), el.rpow(er, axis=0)) pytest.raises(ValueError, lambda: l.add(r, axis=1)) for l, r, el, er in [(ddf1, pdf2, pdf1, pdf2), (ddf1, ps3, pdf1, ps3)]: assert_eq(l, el) assert_eq(r, er) for axis in [0, 1, 'index', 'columns']: assert_eq(l.add(r, axis=axis), el.add(er, axis=axis)) assert_eq(l.sub(r, axis=axis), el.sub(er, axis=axis)) assert_eq(l.mul(r, axis=axis), el.mul(er, axis=axis)) assert_eq(l.div(r, axis=axis), el.div(er, axis=axis)) assert_eq(l.truediv(r, axis=axis), el.truediv(er, axis=axis)) assert_eq(l.floordiv(r, axis=axis), el.floordiv(er, axis=axis)) assert_eq(l.mod(r, axis=axis), el.mod(er, axis=axis)) assert_eq(l.pow(r, axis=axis), el.pow(er, axis=axis)) assert_eq(l.radd(r, axis=axis), el.radd(er, axis=axis)) assert_eq(l.rsub(r, axis=axis), el.rsub(er, axis=axis)) assert_eq(l.rmul(r, axis=axis), el.rmul(er, axis=axis)) assert_eq(l.rdiv(r, axis=axis), el.rdiv(er, axis=axis)) assert_eq(l.rtruediv(r, axis=axis), el.rtruediv(er, axis=axis)) assert_eq(l.rfloordiv(r, axis=axis), el.rfloordiv(er, axis=axis)) assert_eq(l.rmod(r, axis=axis), el.rmod(er, axis=axis)) assert_eq(l.rpow(r, axis=axis), el.rpow(er, axis=axis)) @pytest.mark.parametrize('split_every', [False, 2]) def test_reductions(split_every): dsk = {('x', 0): pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}, index=[0, 1, 3]), ('x', 1): pd.DataFrame({'a': [4, 5, 6], 'b': [3, 2, 1]}, index=[5, 6, 8]), ('x', 2): pd.DataFrame({'a': [7, 8, 9], 'b': [0, 0, 0]}, index=[9, 9, 9])} meta = make_meta({'a': 'i8', 'b': 'i8'}, index=pd.Index([], 'i8')) ddf1 = dd.DataFrame(dsk, 'x', meta, [0, 4, 9, 9]) pdf1 = ddf1.compute() nans1 = pd.Series([1] + [np.nan] * 4 + [2] + [np.nan] * 3) nands1 = dd.from_pandas(nans1, 2) nans2 = pd.Series([1] + [np.nan] * 8) nands2 = dd.from_pandas(nans2, 2) nans3 = pd.Series([np.nan] * 9) nands3 = dd.from_pandas(nans3, 2) bools = pd.Series([True, False, True, False, True], dtype=bool) boolds = dd.from_pandas(bools, 2) for dds, pds in [(ddf1.b, pdf1.b), (ddf1.a, pdf1.a), (ddf1['a'], pdf1['a']), (ddf1['b'], pdf1['b']), (nands1, nans1), (nands2, nans2), (nands3, nans3), (boolds, bools)]: assert isinstance(dds, dd.Series) assert isinstance(pds, pd.Series) assert_eq(dds.sum(split_every=split_every), pds.sum()) assert_eq(dds.prod(split_every=split_every), pds.prod()) assert_eq(dds.min(split_every=split_every), pds.min()) assert_eq(dds.max(split_every=split_every), pds.max()) assert_eq(dds.count(split_every=split_every), pds.count()) with pytest.warns(None): # runtime warnings; https://github.com/dask/dask/issues/2381 assert_eq(dds.std(split_every=split_every), pds.std()) with pytest.warns(None): # runtime warnings; https://github.com/dask/dask/issues/2381 assert_eq(dds.var(split_every=split_every), pds.var()) with pytest.warns(None): # runtime warnings; https://github.com/dask/dask/issues/2381 assert_eq(dds.sem(split_every=split_every), pds.sem()) assert_eq(dds.std(ddof=0, split_every=split_every), pds.std(ddof=0)) assert_eq(dds.var(ddof=0, split_every=split_every), pds.var(ddof=0)) assert_eq(dds.sem(ddof=0, split_every=split_every), pds.sem(ddof=0)) assert_eq(dds.mean(split_every=split_every), pds.mean()) assert_eq(dds.nunique(split_every=split_every), pds.nunique()) assert_eq(dds.sum(skipna=False, split_every=split_every), pds.sum(skipna=False)) assert_eq(dds.prod(skipna=False, split_every=split_every), pds.prod(skipna=False)) assert_eq(dds.min(skipna=False, split_every=split_every), pds.min(skipna=False)) assert_eq(dds.max(skipna=False, split_every=split_every), pds.max(skipna=False)) assert_eq(dds.std(skipna=False, split_every=split_every), pds.std(skipna=False)) assert_eq(dds.var(skipna=False, split_every=split_every), pds.var(skipna=False)) assert_eq(dds.sem(skipna=False, split_every=split_every), pds.sem(skipna=False)) assert_eq(dds.std(skipna=False, ddof=0, split_every=split_every), pds.std(skipna=False, ddof=0)) assert_eq(dds.var(skipna=False, ddof=0, split_every=split_every), pds.var(skipna=False, ddof=0)) assert_eq(dds.sem(skipna=False, ddof=0, split_every=split_every), pds.sem(skipna=False, ddof=0)) assert_eq(dds.mean(skipna=False, split_every=split_every), pds.mean(skipna=False)) assert_dask_graph(ddf1.b.sum(split_every=split_every), 'series-sum') assert_dask_graph(ddf1.b.prod(split_every=split_every), 'series-prod') assert_dask_graph(ddf1.b.min(split_every=split_every), 'series-min') assert_dask_graph(ddf1.b.max(split_every=split_every), 'series-max') assert_dask_graph(ddf1.b.count(split_every=split_every), 'series-count') assert_dask_graph(ddf1.b.std(split_every=split_every), 'series-std') assert_dask_graph(ddf1.b.var(split_every=split_every), 'series-var') assert_dask_graph(ddf1.b.sem(split_every=split_every), 'series-sem') assert_dask_graph(ddf1.b.std(ddof=0, split_every=split_every), 'series-std') assert_dask_graph(ddf1.b.var(ddof=0, split_every=split_every), 'series-var') assert_dask_graph(ddf1.b.sem(ddof=0, split_every=split_every), 'series-sem') assert_dask_graph(ddf1.b.mean(split_every=split_every), 'series-mean') # nunique is performed using drop-duplicates assert_dask_graph(ddf1.b.nunique(split_every=split_every), 'drop-duplicates') # testing index assert_eq(ddf1.index.min(split_every=split_every), pdf1.index.min()) assert_eq(ddf1.index.max(split_every=split_every), pdf1.index.max()) assert_eq(ddf1.index.count(split_every=split_every), pd.notnull(pdf1.index).sum()) @pytest.mark.parametrize('frame,axis,out', [(pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}, index=[0, 1, 3]), 0, pd.Series([])), (pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}, index=[0, 1, 3]), 1, pd.Series([])), (pd.Series([1, 2.5, 6]), None, None)]) @pytest.mark.parametrize('redfunc', ['sum', 'prod', 'min', 'max', 'mean', 'var', 'std']) def test_reductions_out(frame, axis, out, redfunc): dsk_in = dd.from_pandas(frame, 3) dsk_out = dd.from_pandas(pd.Series([0]), 1).sum() if out is not None: dsk_out = dd.from_pandas(out, 3) np_redfunc = getattr(np, redfunc) pd_redfunc = getattr(frame.__class__, redfunc) dsk_redfunc = getattr(dsk_in.__class__, redfunc) if redfunc in ['var', 'std']: # numpy has default ddof value 0 while # dask and pandas have 1, so ddof should be passed # explicitly when calling np.var(dask) np_redfunc(dsk_in, axis=axis, ddof=1, out=dsk_out) else: np_redfunc(dsk_in, axis=axis, out=dsk_out) assert_eq(dsk_out, pd_redfunc(frame, axis=axis)) dsk_redfunc(dsk_in, axis=axis, split_every=False, out=dsk_out) assert_eq(dsk_out, pd_redfunc(frame, axis=axis)) dsk_redfunc(dsk_in, axis=axis, split_every=2, out=dsk_out) assert_eq(dsk_out, pd_redfunc(frame, axis=axis)) @pytest.mark.parametrize('split_every', [False, 2]) def test_allany(split_every): df = pd.DataFrame(np.random.choice([True, False], size=(100, 4)), columns=['A', 'B', 'C', 'D']) df['E'] = list('abcde') * 20 ddf = dd.from_pandas(df, 10) assert_eq(ddf.all(split_every=split_every), df.all()) assert_eq(ddf.all(axis=1, split_every=split_every), df.all(axis=1)) assert_eq(ddf.all(axis=0, split_every=split_every), df.all(axis=0)) assert_eq(ddf.any(split_every=split_every), df.any()) assert_eq(ddf.any(axis=1, split_every=split_every), df.any(axis=1)) assert_eq(ddf.any(axis=0, split_every=split_every), df.any(axis=0)) assert_eq(ddf.A.all(split_every=split_every), df.A.all()) assert_eq(ddf.A.any(split_every=split_every), df.A.any()) # testing numpy functions with out param ddf_out_axis_default = dd.from_pandas(pd.Series([False, False, False, False, False], index=['A', 'B', 'C', 'D', 'E']), 10) ddf_out_axis1 = dd.from_pandas(pd.Series(np.random.choice([True, False], size=(100,))), 10) # all ddf.all(split_every=split_every, out=ddf_out_axis_default) assert_eq(ddf_out_axis_default, df.all()) ddf.all(axis=1, split_every=split_every, out=ddf_out_axis1) assert_eq(ddf_out_axis1, df.all(axis=1)) ddf.all(split_every=split_every, axis=0, out=ddf_out_axis_default) assert_eq(ddf_out_axis_default, df.all(axis=0)) # any ddf.any(split_every=split_every, out=ddf_out_axis_default) assert_eq(ddf_out_axis_default, df.any()) ddf.any(axis=1, split_every=split_every, out=ddf_out_axis1) assert_eq(ddf_out_axis1, df.any(axis=1)) ddf.any(split_every=split_every, axis=0, out=ddf_out_axis_default) assert_eq(ddf_out_axis_default, df.any(axis=0)) @pytest.mark.parametrize('split_every', [False, 2]) def test_deterministic_reduction_names(split_every): df = pd.DataFrame({'x': [1, 2, 3, 4], 'y': [5, 6, 7, 8]}) ddf = dd.from_pandas(df, npartitions=2) for x in [ddf, ddf.x]: assert (x.sum(split_every=split_every)._name == x.sum(split_every=split_every)._name) assert (x.prod(split_every=split_every)._name == x.prod(split_every=split_every)._name) assert (x.min(split_every=split_every)._name == x.min(split_every=split_every)._name) assert (x.max(split_every=split_every)._name == x.max(split_every=split_every)._name) assert (x.count(split_every=split_every)._name == x.count(split_every=split_every)._name) assert (x.std(split_every=split_every)._name == x.std(split_every=split_every)._name) assert (x.var(split_every=split_every)._name == x.var(split_every=split_every)._name) assert (x.sem(split_every=split_every)._name == x.sem(split_every=split_every)._name) assert (x.mean(split_every=split_every)._name == x.mean(split_every=split_every)._name) assert (ddf.x.nunique(split_every=split_every)._name == ddf.x.nunique(split_every=split_every)._name) def test_reduction_series_invalid_axis(): dsk = {('x', 0): pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}, index=[0, 1, 3]), ('x', 1): pd.DataFrame({'a': [4, 5, 6], 'b': [3, 2, 1]}, index=[5, 6, 8]), ('x', 2): pd.DataFrame({'a': [7, 8, 9], 'b': [0, 0, 0]}, index=[9, 9, 9])} meta = make_meta({'a': 'i8', 'b': 'i8'}, index=pd.Index([], 'i8')) ddf1 = dd.DataFrame(dsk, 'x', meta, [0, 4, 9, 9]) pdf1 = ddf1.compute() for axis in [1, 'columns']: for s in [ddf1.a, pdf1.a]: # both must behave the same pytest.raises(ValueError, lambda: s.sum(axis=axis)) pytest.raises(ValueError, lambda: s.prod(axis=axis)) pytest.raises(ValueError, lambda: s.min(axis=axis)) pytest.raises(ValueError, lambda: s.max(axis=axis)) # only count doesn't have axis keyword pytest.raises(TypeError, lambda: s.count(axis=axis)) pytest.raises(ValueError, lambda: s.std(axis=axis)) pytest.raises(ValueError, lambda: s.var(axis=axis)) pytest.raises(ValueError, lambda: s.sem(axis=axis)) pytest.raises(ValueError, lambda: s.mean(axis=axis)) def test_reductions_non_numeric_dtypes(): # test non-numric blocks def check_raises(d, p, func): pytest.raises((TypeError, ValueError), lambda: getattr(d, func)().compute()) pytest.raises((TypeError, ValueError), lambda: getattr(p, func)()) pds = pd.Series(['a', 'b', 'c', 'd', 'e']) dds = dd.from_pandas(pds, 2) assert_eq(dds.sum(), pds.sum()) check_raises(dds, pds, 'prod') assert_eq(dds.min(), pds.min()) assert_eq(dds.max(), pds.max()) assert_eq(dds.count(), pds.count()) check_raises(dds, pds, 'std') check_raises(dds, pds, 'var') check_raises(dds, pds, 'sem') check_raises(dds, pds, 'mean') assert_eq(dds.nunique(), pds.nunique()) for pds in [pd.Series(pd.Categorical([1, 2, 3, 4, 5], ordered=True)), pd.Series(pd.Categorical(list('abcde'), ordered=True)), pd.Series(pd.date_range('2011-01-01', freq='D', periods=5))]: dds = dd.from_pandas(pds, 2) check_raises(dds, pds, 'sum') check_raises(dds, pds, 'prod') assert_eq(dds.min(), pds.min()) assert_eq(dds.max(), pds.max()) assert_eq(dds.count(), pds.count()) check_raises(dds, pds, 'std') check_raises(dds, pds, 'var') check_raises(dds, pds, 'sem') check_raises(dds, pds, 'mean') assert_eq(dds.nunique(), pds.nunique()) pds = pd.Series(pd.timedelta_range('1 days', freq='D', periods=5)) dds = dd.from_pandas(pds, 2) assert_eq(dds.sum(), pds.sum()) assert_eq(dds.min(), pds.min()) assert_eq(dds.max(), pds.max()) assert_eq(dds.count(), pds.count()) # ToDo: pandas supports timedelta std, otherwise dask raises: # incompatible type for a datetime/timedelta operation [__pow__] # assert_eq(dds.std(), pds.std()) # assert_eq(dds.var(), pds.var()) # ToDo: pandas supports timedelta std, otherwise dask raises: # TypeError: unsupported operand type(s) for *: 'float' and 'Timedelta' # assert_eq(dds.mean(), pds.mean()) assert_eq(dds.nunique(), pds.nunique()) @pytest.mark.parametrize('split_every', [False, 2]) def test_reductions_frame(split_every): dsk = {('x', 0): pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}, index=[0, 1, 3]), ('x', 1): pd.DataFrame({'a': [4, 5, 6], 'b': [3, 2, 1]}, index=[5, 6, 8]), ('x', 2): pd.DataFrame({'a': [7, 8, 9], 'b': [0, 0, 0]}, index=[9, 9, 9])} meta = make_meta({'a': 'i8', 'b': 'i8'}, index=pd.Index([], 'i8')) ddf1 = dd.DataFrame(dsk, 'x', meta, [0, 4, 9, 9]) pdf1 = ddf1.compute() assert_eq(ddf1.sum(split_every=split_every), pdf1.sum()) assert_eq(ddf1.prod(split_every=split_every), pdf1.prod()) assert_eq(ddf1.min(split_every=split_every), pdf1.min()) assert_eq(ddf1.max(split_every=split_every), pdf1.max()) assert_eq(ddf1.count(split_every=split_every), pdf1.count()) assert_eq(ddf1.std(split_every=split_every), pdf1.std()) assert_eq(ddf1.var(split_every=split_every), pdf1.var()) assert_eq(ddf1.sem(split_every=split_every), pdf1.sem()) assert_eq(ddf1.std(ddof=0, split_every=split_every), pdf1.std(ddof=0)) assert_eq(ddf1.var(ddof=0, split_every=split_every), pdf1.var(ddof=0)) assert_eq(ddf1.sem(ddof=0, split_every=split_every), pdf1.sem(ddof=0)) assert_eq(ddf1.mean(split_every=split_every), pdf1.mean()) for axis in [0, 1, 'index', 'columns']: assert_eq(ddf1.sum(axis=axis, split_every=split_every), pdf1.sum(axis=axis)) assert_eq(ddf1.prod(axis=axis, split_every=split_every), pdf1.prod(axis=axis)) assert_eq(ddf1.min(axis=axis, split_every=split_every), pdf1.min(axis=axis)) assert_eq(ddf1.max(axis=axis, split_every=split_every), pdf1.max(axis=axis)) assert_eq(ddf1.count(axis=axis, split_every=split_every), pdf1.count(axis=axis)) assert_eq(ddf1.std(axis=axis, split_every=split_every), pdf1.std(axis=axis)) assert_eq(ddf1.var(axis=axis, split_every=split_every), pdf1.var(axis=axis)) assert_eq(ddf1.sem(axis=axis, split_every=split_every), pdf1.sem(axis=axis)) assert_eq(ddf1.std(axis=axis, ddof=0, split_every=split_every), pdf1.std(axis=axis, ddof=0)) assert_eq(ddf1.var(axis=axis, ddof=0, split_every=split_every), pdf1.var(axis=axis, ddof=0)) assert_eq(ddf1.sem(axis=axis, ddof=0, split_every=split_every), pdf1.sem(axis=axis, ddof=0)) assert_eq(ddf1.mean(axis=axis, split_every=split_every), pdf1.mean(axis=axis)) pytest.raises(ValueError, lambda: ddf1.sum(axis='incorrect').compute()) # axis=0 assert_dask_graph(ddf1.sum(split_every=split_every), 'dataframe-sum') assert_dask_graph(ddf1.prod(split_every=split_every), 'dataframe-prod') assert_dask_graph(ddf1.min(split_every=split_every), 'dataframe-min') assert_dask_graph(ddf1.max(split_every=split_every), 'dataframe-max') assert_dask_graph(ddf1.count(split_every=split_every), 'dataframe-count') # std, var, sem, and mean consist of sum and count operations assert_dask_graph(ddf1.std(split_every=split_every), 'dataframe-sum') assert_dask_graph(ddf1.std(split_every=split_every), 'dataframe-count') assert_dask_graph(ddf1.var(split_every=split_every), 'dataframe-sum') assert_dask_graph(ddf1.var(split_every=split_every), 'dataframe-count') assert_dask_graph(ddf1.sem(split_every=split_every), 'dataframe-sum') assert_dask_graph(ddf1.sem(split_every=split_every), 'dataframe-count') assert_dask_graph(ddf1.mean(split_every=split_every), 'dataframe-sum') assert_dask_graph(ddf1.mean(split_every=split_every), 'dataframe-count') # axis=1 assert_dask_graph(ddf1.sum(axis=1, split_every=split_every), 'dataframe-sum') assert_dask_graph(ddf1.prod(axis=1, split_every=split_every), 'dataframe-prod') assert_dask_graph(ddf1.min(axis=1, split_every=split_every), 'dataframe-min') assert_dask_graph(ddf1.max(axis=1, split_every=split_every), 'dataframe-max') assert_dask_graph(ddf1.count(axis=1, split_every=split_every), 'dataframe-count') assert_dask_graph(ddf1.std(axis=1, split_every=split_every), 'dataframe-std') assert_dask_graph(ddf1.var(axis=1, split_every=split_every), 'dataframe-var') assert_dask_graph(ddf1.sem(axis=1, split_every=split_every), 'dataframe-sem') assert_dask_graph(ddf1.mean(axis=1, split_every=split_every), 'dataframe-mean') def test_reductions_frame_dtypes(): df = pd.DataFrame({'int': [1, 2, 3, 4, 5, 6, 7, 8], 'float': [1., 2., 3., 4., np.nan, 6., 7., 8.], 'dt': [pd.NaT] + [datetime(2011, i, 1) for i in range(1, 8)], 'str': list('abcdefgh')}) if HAS_INT_NA: df['intna'] = pd.array([1, 2, 3, 4, None, 6, 7, 8], dtype=pd.Int64Dtype()) ddf = dd.from_pandas(df, 3) assert_eq(df.sum(), ddf.sum()) assert_eq(df.prod(), ddf.prod()) assert_eq(df.min(), ddf.min()) assert_eq(df.max(), ddf.max()) assert_eq(df.count(), ddf.count()) assert_eq(df.std(), ddf.std()) assert_eq(df.var(), ddf.var()) assert_eq(df.sem(), ddf.sem()) assert_eq(df.std(ddof=0), ddf.std(ddof=0)) assert_eq(df.var(ddof=0), ddf.var(ddof=0)) assert_eq(df.sem(ddof=0), ddf.sem(ddof=0)) result = ddf.mean() expected = df.mean() assert_eq(expected, result) assert_eq(df._get_numeric_data(), ddf._get_numeric_data()) numerics = ddf[['int', 'float']] assert numerics._get_numeric_data().dask == numerics.dask @pytest.mark.parametrize('split_every', [False, 2]) def test_reductions_frame_nan(split_every): df = pd.DataFrame({'a': [1, 2, np.nan, 4, 5, 6, 7, 8], 'b': [1, 2, np.nan, np.nan, np.nan, 5, np.nan, np.nan], 'c': [np.nan] * 8}) ddf = dd.from_pandas(df, 3) assert_eq(df.sum(), ddf.sum(split_every=split_every)) assert_eq(df.prod(), ddf.prod(split_every=split_every)) assert_eq(df.min(), ddf.min(split_every=split_every)) assert_eq(df.max(), ddf.max(split_every=split_every)) assert_eq(df.count(), ddf.count(split_every=split_every)) assert_eq(df.std(), ddf.std(split_every=split_every)) assert_eq(df.var(), ddf.var(split_every=split_every)) assert_eq(df.sem(), ddf.sem(split_every=split_every)) assert_eq(df.std(ddof=0), ddf.std(ddof=0, split_every=split_every)) assert_eq(df.var(ddof=0), ddf.var(ddof=0, split_every=split_every)) assert_eq(df.sem(ddof=0), ddf.sem(ddof=0, split_every=split_every)) assert_eq(df.mean(), ddf.mean(split_every=split_every)) with warnings.catch_warnings(record=True): assert_eq(df.sum(skipna=False), ddf.sum(skipna=False, split_every=split_every)) assert_eq(df.prod(skipna=False), ddf.prod(skipna=False, split_every=split_every)) assert_eq(df.min(skipna=False), ddf.min(skipna=False, split_every=split_every)) assert_eq(df.max(skipna=False), ddf.max(skipna=False, split_every=split_every)) assert_eq(df.std(skipna=False), ddf.std(skipna=False, split_every=split_every)) assert_eq(df.var(skipna=False), ddf.var(skipna=False, split_every=split_every)) assert_eq(df.sem(skipna=False), ddf.sem(skipna=False, split_every=split_every)) assert_eq(df.std(skipna=False, ddof=0), ddf.std(skipna=False, ddof=0, split_every=split_every)) assert_eq(df.var(skipna=False, ddof=0), ddf.var(skipna=False, ddof=0, split_every=split_every)) assert_eq(df.sem(skipna=False, ddof=0), ddf.sem(skipna=False, ddof=0, split_every=split_every)) assert_eq(df.mean(skipna=False), ddf.mean(skipna=False, split_every=split_every)) assert_eq(df.sum(axis=1, skipna=False), ddf.sum(axis=1, skipna=False, split_every=split_every)) assert_eq(df.prod(axis=1, skipna=False), ddf.prod(axis=1, skipna=False, split_every=split_every)) assert_eq(df.min(axis=1, skipna=False), ddf.min(axis=1, skipna=False, split_every=split_every)) assert_eq(df.max(axis=1, skipna=False), ddf.max(axis=1, skipna=False, split_every=split_every)) assert_eq(df.std(axis=1, skipna=False), ddf.std(axis=1, skipna=False, split_every=split_every)) assert_eq(df.var(axis=1, skipna=False), ddf.var(axis=1, skipna=False, split_every=split_every)) assert_eq(df.sem(axis=1, skipna=False), ddf.sem(axis=1, skipna=False, split_every=split_every)) assert_eq(df.std(axis=1, skipna=False, ddof=0), ddf.std(axis=1, skipna=False, ddof=0, split_every=split_every)) assert_eq(df.var(axis=1, skipna=False, ddof=0), ddf.var(axis=1, skipna=False, ddof=0, split_every=split_every)) assert_eq(df.sem(axis=1, skipna=False, ddof=0), ddf.sem(axis=1, skipna=False, ddof=0, split_every=split_every)) assert_eq(df.mean(axis=1, skipna=False), ddf.mean(axis=1, skipna=False, split_every=split_every)) @pytest.mark.parametrize('comparison', ['lt', 'gt', 'le', 'ge', 'ne', 'eq']) def test_series_comparison_nan(comparison): s = pd.Series([1, 2, 3, 4, 5, 6, 7]) s_nan = pd.Series([1, -1, 8, np.nan, 5, 6, 2.4]) ds = dd.from_pandas(s, 3) ds_nan = dd.from_pandas(s_nan, 3) fill_value = 7 comparison_pd = getattr(s, comparison) comparison_dd = getattr(ds, comparison) assert_eq(comparison_dd(ds_nan, fill_value=fill_value), comparison_pd(s_nan, fill_value=fill_value)) skip_if_no_intna = pytest.mark.skipif(not HAS_INT_NA, reason="integer na") @skip_if_no_intna def test_sum_intna(): a = pd.Series([1, None, 2], dtype=pd.Int32Dtype()) b = dd.from_pandas(a, 2) assert_eq(a.sum(), b.sum())