from datetime import timedelta from decimal import Decimal import operator import numpy as np import pytest import pandas.util._test_decorators as td import pandas as pd from pandas import ( Categorical, DataFrame, MultiIndex, Series, Timestamp, date_range, isna, notna, to_datetime, to_timedelta, ) import pandas._testing as tm import pandas.core.algorithms as algorithms import pandas.core.nanops as nanops def assert_stat_op_calc( opname, alternative, frame, has_skipna=True, check_dtype=True, check_dates=False, rtol=1e-5, atol=1e-8, skipna_alternative=None, ): """ Check that operator opname works as advertised on frame Parameters ---------- opname : string Name of the operator to test on frame alternative : function Function that opname is tested against; i.e. "frame.opname()" should equal "alternative(frame)". frame : DataFrame The object that the tests are executed on has_skipna : bool, default True Whether the method "opname" has the kwarg "skip_na" check_dtype : bool, default True Whether the dtypes of the result of "frame.opname()" and "alternative(frame)" should be checked. check_dates : bool, default false Whether opname should be tested on a Datetime Series rtol : float, default 1e-5 Relative tolerance. atol : float, default 1e-8 Absolute tolerance. skipna_alternative : function, default None NaN-safe version of alternative """ f = getattr(frame, opname) if check_dates: expected_warning = FutureWarning if opname in ["mean", "median"] else None df = DataFrame({"b": date_range("1/1/2001", periods=2)}) with tm.assert_produces_warning(expected_warning): result = getattr(df, opname)() assert isinstance(result, Series) df["a"] = range(len(df)) with tm.assert_produces_warning(expected_warning): result = getattr(df, opname)() assert isinstance(result, Series) assert len(result) if has_skipna: def wrapper(x): return alternative(x.values) skipna_wrapper = tm._make_skipna_wrapper(alternative, skipna_alternative) result0 = f(axis=0, skipna=False) result1 = f(axis=1, skipna=False) tm.assert_series_equal( result0, frame.apply(wrapper), check_dtype=check_dtype, rtol=rtol, atol=atol, ) # HACK: win32 tm.assert_series_equal( result1, frame.apply(wrapper, axis=1), check_dtype=False, rtol=rtol, atol=atol, ) else: skipna_wrapper = alternative result0 = f(axis=0) result1 = f(axis=1) tm.assert_series_equal( result0, frame.apply(skipna_wrapper), check_dtype=check_dtype, rtol=rtol, atol=atol, ) if opname in ["sum", "prod"]: expected = frame.apply(skipna_wrapper, axis=1) tm.assert_series_equal( result1, expected, check_dtype=False, rtol=rtol, atol=atol, ) # check dtypes if check_dtype: lcd_dtype = frame.values.dtype assert lcd_dtype == result0.dtype assert lcd_dtype == result1.dtype # bad axis with pytest.raises(ValueError, match="No axis named 2"): f(axis=2) # all NA case if has_skipna: all_na = frame * np.NaN r0 = getattr(all_na, opname)(axis=0) r1 = getattr(all_na, opname)(axis=1) if opname in ["sum", "prod"]: unit = 1 if opname == "prod" else 0 # result for empty sum/prod expected = pd.Series(unit, index=r0.index, dtype=r0.dtype) tm.assert_series_equal(r0, expected) expected = pd.Series(unit, index=r1.index, dtype=r1.dtype) tm.assert_series_equal(r1, expected) def assert_stat_op_api(opname, float_frame, float_string_frame, has_numeric_only=False): """ Check that API for operator opname works as advertised on frame Parameters ---------- opname : string Name of the operator to test on frame float_frame : DataFrame DataFrame with columns of type float float_string_frame : DataFrame DataFrame with both float and string columns has_numeric_only : bool, default False Whether the method "opname" has the kwarg "numeric_only" """ # make sure works on mixed-type frame getattr(float_string_frame, opname)(axis=0) getattr(float_string_frame, opname)(axis=1) if has_numeric_only: getattr(float_string_frame, opname)(axis=0, numeric_only=True) getattr(float_string_frame, opname)(axis=1, numeric_only=True) getattr(float_frame, opname)(axis=0, numeric_only=False) getattr(float_frame, opname)(axis=1, numeric_only=False) def assert_bool_op_calc(opname, alternative, frame, has_skipna=True): """ Check that bool operator opname works as advertised on frame Parameters ---------- opname : string Name of the operator to test on frame alternative : function Function that opname is tested against; i.e. "frame.opname()" should equal "alternative(frame)". frame : DataFrame The object that the tests are executed on has_skipna : bool, default True Whether the method "opname" has the kwarg "skip_na" """ f = getattr(frame, opname) if has_skipna: def skipna_wrapper(x): nona = x.dropna().values return alternative(nona) def wrapper(x): return alternative(x.values) result0 = f(axis=0, skipna=False) result1 = f(axis=1, skipna=False) tm.assert_series_equal(result0, frame.apply(wrapper)) tm.assert_series_equal( result1, frame.apply(wrapper, axis=1), check_dtype=False ) # HACK: win32 else: skipna_wrapper = alternative wrapper = alternative result0 = f(axis=0) result1 = f(axis=1) tm.assert_series_equal(result0, frame.apply(skipna_wrapper)) tm.assert_series_equal( result1, frame.apply(skipna_wrapper, axis=1), check_dtype=False ) # bad axis with pytest.raises(ValueError, match="No axis named 2"): f(axis=2) # all NA case if has_skipna: all_na = frame * np.NaN r0 = getattr(all_na, opname)(axis=0) r1 = getattr(all_na, opname)(axis=1) if opname == "any": assert not r0.any() assert not r1.any() else: assert r0.all() assert r1.all() def assert_bool_op_api( opname, bool_frame_with_na, float_string_frame, has_bool_only=False ): """ Check that API for boolean operator opname works as advertised on frame Parameters ---------- opname : string Name of the operator to test on frame float_frame : DataFrame DataFrame with columns of type float float_string_frame : DataFrame DataFrame with both float and string columns has_bool_only : bool, default False Whether the method "opname" has the kwarg "bool_only" """ # make sure op works on mixed-type frame mixed = float_string_frame mixed["_bool_"] = np.random.randn(len(mixed)) > 0.5 getattr(mixed, opname)(axis=0) getattr(mixed, opname)(axis=1) if has_bool_only: getattr(mixed, opname)(axis=0, bool_only=True) getattr(mixed, opname)(axis=1, bool_only=True) getattr(bool_frame_with_na, opname)(axis=0, bool_only=False) getattr(bool_frame_with_na, opname)(axis=1, bool_only=False) class TestDataFrameAnalytics: # --------------------------------------------------------------------- # Reductions def test_stat_op_api(self, float_frame, float_string_frame): assert_stat_op_api( "count", float_frame, float_string_frame, has_numeric_only=True ) assert_stat_op_api( "sum", float_frame, float_string_frame, has_numeric_only=True ) assert_stat_op_api("nunique", float_frame, float_string_frame) assert_stat_op_api("mean", float_frame, float_string_frame) assert_stat_op_api("product", float_frame, float_string_frame) assert_stat_op_api("median", float_frame, float_string_frame) assert_stat_op_api("min", float_frame, float_string_frame) assert_stat_op_api("max", float_frame, float_string_frame) assert_stat_op_api("mad", float_frame, float_string_frame) assert_stat_op_api("var", float_frame, float_string_frame) assert_stat_op_api("std", float_frame, float_string_frame) assert_stat_op_api("sem", float_frame, float_string_frame) assert_stat_op_api("median", float_frame, float_string_frame) try: from scipy.stats import kurtosis, skew # noqa:F401 assert_stat_op_api("skew", float_frame, float_string_frame) assert_stat_op_api("kurt", float_frame, float_string_frame) except ImportError: pass def test_stat_op_calc(self, float_frame_with_na, mixed_float_frame): def count(s): return notna(s).sum() def nunique(s): return len(algorithms.unique1d(s.dropna())) def mad(x): return np.abs(x - x.mean()).mean() def var(x): return np.var(x, ddof=1) def std(x): return np.std(x, ddof=1) def sem(x): return np.std(x, ddof=1) / np.sqrt(len(x)) def skewness(x): from scipy.stats import skew # noqa:F811 if len(x) < 3: return np.nan return skew(x, bias=False) def kurt(x): from scipy.stats import kurtosis # noqa:F811 if len(x) < 4: return np.nan return kurtosis(x, bias=False) assert_stat_op_calc( "nunique", nunique, float_frame_with_na, has_skipna=False, check_dtype=False, check_dates=True, ) # GH#32571 check_less_precise is needed on apparently-random # py37-npdev builds and OSX-PY36-min_version builds # mixed types (with upcasting happening) assert_stat_op_calc( "sum", np.sum, mixed_float_frame.astype("float32"), check_dtype=False, rtol=1e-3, ) assert_stat_op_calc( "sum", np.sum, float_frame_with_na, skipna_alternative=np.nansum ) assert_stat_op_calc("mean", np.mean, float_frame_with_na, check_dates=True) assert_stat_op_calc( "product", np.prod, float_frame_with_na, skipna_alternative=np.nanprod ) assert_stat_op_calc("mad", mad, float_frame_with_na) assert_stat_op_calc("var", var, float_frame_with_na) assert_stat_op_calc("std", std, float_frame_with_na) assert_stat_op_calc("sem", sem, float_frame_with_na) assert_stat_op_calc( "count", count, float_frame_with_na, has_skipna=False, check_dtype=False, check_dates=True, ) try: from scipy import kurtosis, skew # noqa:F401 assert_stat_op_calc("skew", skewness, float_frame_with_na) assert_stat_op_calc("kurt", kurt, float_frame_with_na) except ImportError: pass # TODO: Ensure warning isn't emitted in the first place @pytest.mark.filterwarnings("ignore:All-NaN:RuntimeWarning") def test_median(self, float_frame_with_na, int_frame): def wrapper(x): if isna(x).any(): return np.nan return np.median(x) assert_stat_op_calc("median", wrapper, float_frame_with_na, check_dates=True) assert_stat_op_calc( "median", wrapper, int_frame, check_dtype=False, check_dates=True ) @pytest.mark.parametrize( "method", ["sum", "mean", "prod", "var", "std", "skew", "min", "max"] ) def test_stat_operators_attempt_obj_array(self, method): # GH#676 data = { "a": [ -0.00049987540199591344, -0.0016467257772919831, 0.00067695870775883013, ], "b": [-0, -0, 0.0], "c": [ 0.00031111847529610595, 0.0014902627951905339, -0.00094099200035979691, ], } df1 = DataFrame(data, index=["foo", "bar", "baz"], dtype="O") df2 = DataFrame({0: [np.nan, 2], 1: [np.nan, 3], 2: [np.nan, 4]}, dtype=object) for df in [df1, df2]: assert df.values.dtype == np.object_ result = getattr(df, method)(1) expected = getattr(df.astype("f8"), method)(1) if method in ["sum", "prod"]: tm.assert_series_equal(result, expected) @pytest.mark.parametrize("op", ["mean", "std", "var", "skew", "kurt", "sem"]) def test_mixed_ops(self, op): # GH#16116 df = DataFrame( { "int": [1, 2, 3, 4], "float": [1.0, 2.0, 3.0, 4.0], "str": ["a", "b", "c", "d"], } ) result = getattr(df, op)() assert len(result) == 2 with pd.option_context("use_bottleneck", False): result = getattr(df, op)() assert len(result) == 2 def test_reduce_mixed_frame(self): # GH 6806 df = DataFrame( { "bool_data": [True, True, False, False, False], "int_data": [10, 20, 30, 40, 50], "string_data": ["a", "b", "c", "d", "e"], } ) df.reindex(columns=["bool_data", "int_data", "string_data"]) test = df.sum(axis=0) tm.assert_numpy_array_equal( test.values, np.array([2, 150, "abcde"], dtype=object) ) tm.assert_series_equal(test, df.T.sum(axis=1)) def test_nunique(self): df = DataFrame({"A": [1, 1, 1], "B": [1, 2, 3], "C": [1, np.nan, 3]}) tm.assert_series_equal(df.nunique(), Series({"A": 1, "B": 3, "C": 2})) tm.assert_series_equal( df.nunique(dropna=False), Series({"A": 1, "B": 3, "C": 3}) ) tm.assert_series_equal(df.nunique(axis=1), Series({0: 1, 1: 2, 2: 2})) tm.assert_series_equal( df.nunique(axis=1, dropna=False), Series({0: 1, 1: 3, 2: 2}) ) @pytest.mark.parametrize("tz", [None, "UTC"]) def test_mean_mixed_datetime_numeric(self, tz): # https://github.com/pandas-dev/pandas/issues/24752 df = pd.DataFrame({"A": [1, 1], "B": [pd.Timestamp("2000", tz=tz)] * 2}) with tm.assert_produces_warning(FutureWarning): result = df.mean() expected = pd.Series([1.0], index=["A"]) tm.assert_series_equal(result, expected) @pytest.mark.parametrize("tz", [None, "UTC"]) def test_mean_excludes_datetimes(self, tz): # https://github.com/pandas-dev/pandas/issues/24752 # Our long-term desired behavior is unclear, but the behavior in # 0.24.0rc1 was buggy. df = pd.DataFrame({"A": [pd.Timestamp("2000", tz=tz)] * 2}) with tm.assert_produces_warning(FutureWarning): result = df.mean() expected = pd.Series(dtype=np.float64) tm.assert_series_equal(result, expected) def test_mean_mixed_string_decimal(self): # GH 11670 # possible bug when calculating mean of DataFrame? d = [ {"A": 2, "B": None, "C": Decimal("628.00")}, {"A": 1, "B": None, "C": Decimal("383.00")}, {"A": 3, "B": None, "C": Decimal("651.00")}, {"A": 2, "B": None, "C": Decimal("575.00")}, {"A": 4, "B": None, "C": Decimal("1114.00")}, {"A": 1, "B": "TEST", "C": Decimal("241.00")}, {"A": 2, "B": None, "C": Decimal("572.00")}, {"A": 4, "B": None, "C": Decimal("609.00")}, {"A": 3, "B": None, "C": Decimal("820.00")}, {"A": 5, "B": None, "C": Decimal("1223.00")}, ] df = pd.DataFrame(d) result = df.mean() expected = pd.Series([2.7, 681.6], index=["A", "C"]) tm.assert_series_equal(result, expected) def test_var_std(self, datetime_frame): result = datetime_frame.std(ddof=4) expected = datetime_frame.apply(lambda x: x.std(ddof=4)) tm.assert_almost_equal(result, expected) result = datetime_frame.var(ddof=4) expected = datetime_frame.apply(lambda x: x.var(ddof=4)) tm.assert_almost_equal(result, expected) arr = np.repeat(np.random.random((1, 1000)), 1000, 0) result = nanops.nanvar(arr, axis=0) assert not (result < 0).any() with pd.option_context("use_bottleneck", False): result = nanops.nanvar(arr, axis=0) assert not (result < 0).any() @pytest.mark.parametrize("meth", ["sem", "var", "std"]) def test_numeric_only_flag(self, meth): # GH 9201 df1 = DataFrame(np.random.randn(5, 3), columns=["foo", "bar", "baz"]) # set one entry to a number in str format df1.loc[0, "foo"] = "100" df2 = DataFrame(np.random.randn(5, 3), columns=["foo", "bar", "baz"]) # set one entry to a non-number str df2.loc[0, "foo"] = "a" result = getattr(df1, meth)(axis=1, numeric_only=True) expected = getattr(df1[["bar", "baz"]], meth)(axis=1) tm.assert_series_equal(expected, result) result = getattr(df2, meth)(axis=1, numeric_only=True) expected = getattr(df2[["bar", "baz"]], meth)(axis=1) tm.assert_series_equal(expected, result) # df1 has all numbers, df2 has a letter inside msg = r"unsupported operand type\(s\) for -: 'float' and 'str'" with pytest.raises(TypeError, match=msg): getattr(df1, meth)(axis=1, numeric_only=False) msg = "could not convert string to float: 'a'" with pytest.raises(TypeError, match=msg): getattr(df2, meth)(axis=1, numeric_only=False) def test_sem(self, datetime_frame): result = datetime_frame.sem(ddof=4) expected = datetime_frame.apply(lambda x: x.std(ddof=4) / np.sqrt(len(x))) tm.assert_almost_equal(result, expected) arr = np.repeat(np.random.random((1, 1000)), 1000, 0) result = nanops.nansem(arr, axis=0) assert not (result < 0).any() with pd.option_context("use_bottleneck", False): result = nanops.nansem(arr, axis=0) assert not (result < 0).any() @td.skip_if_no_scipy def test_kurt(self): index = MultiIndex( levels=[["bar"], ["one", "two", "three"], [0, 1]], codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]], ) df = DataFrame(np.random.randn(6, 3), index=index) kurt = df.kurt() kurt2 = df.kurt(level=0).xs("bar") tm.assert_series_equal(kurt, kurt2, check_names=False) assert kurt.name is None assert kurt2.name == "bar" @pytest.mark.parametrize( "dropna, expected", [ ( True, { "A": [12], "B": [10.0], "C": [1.0], "D": ["a"], "E": Categorical(["a"], categories=["a"]), "F": to_datetime(["2000-1-2"]), "G": to_timedelta(["1 days"]), }, ), ( False, { "A": [12], "B": [10.0], "C": [np.nan], "D": np.array([np.nan], dtype=object), "E": Categorical([np.nan], categories=["a"]), "F": [pd.NaT], "G": to_timedelta([pd.NaT]), }, ), ( True, { "H": [8, 9, np.nan, np.nan], "I": [8, 9, np.nan, np.nan], "J": [1, np.nan, np.nan, np.nan], "K": Categorical(["a", np.nan, np.nan, np.nan], categories=["a"]), "L": to_datetime(["2000-1-2", "NaT", "NaT", "NaT"]), "M": to_timedelta(["1 days", "nan", "nan", "nan"]), "N": [0, 1, 2, 3], }, ), ( False, { "H": [8, 9, np.nan, np.nan], "I": [8, 9, np.nan, np.nan], "J": [1, np.nan, np.nan, np.nan], "K": Categorical([np.nan, "a", np.nan, np.nan], categories=["a"]), "L": to_datetime(["NaT", "2000-1-2", "NaT", "NaT"]), "M": to_timedelta(["nan", "1 days", "nan", "nan"]), "N": [0, 1, 2, 3], }, ), ], ) def test_mode_dropna(self, dropna, expected): df = DataFrame( { "A": [12, 12, 19, 11], "B": [10, 10, np.nan, 3], "C": [1, np.nan, np.nan, np.nan], "D": [np.nan, np.nan, "a", np.nan], "E": Categorical([np.nan, np.nan, "a", np.nan]), "F": to_datetime(["NaT", "2000-1-2", "NaT", "NaT"]), "G": to_timedelta(["1 days", "nan", "nan", "nan"]), "H": [8, 8, 9, 9], "I": [9, 9, 8, 8], "J": [1, 1, np.nan, np.nan], "K": Categorical(["a", np.nan, "a", np.nan]), "L": to_datetime(["2000-1-2", "2000-1-2", "NaT", "NaT"]), "M": to_timedelta(["1 days", "nan", "1 days", "nan"]), "N": np.arange(4, dtype="int64"), } ) result = df[sorted(expected.keys())].mode(dropna=dropna) expected = DataFrame(expected) tm.assert_frame_equal(result, expected) def test_mode_sortwarning(self): # Check for the warning that is raised when the mode # results cannot be sorted df = DataFrame({"A": [np.nan, np.nan, "a", "a"]}) expected = DataFrame({"A": ["a", np.nan]}) with tm.assert_produces_warning(UserWarning, check_stacklevel=False): result = df.mode(dropna=False) result = result.sort_values(by="A").reset_index(drop=True) tm.assert_frame_equal(result, expected) def test_operators_timedelta64(self): df = DataFrame( dict( A=date_range("2012-1-1", periods=3, freq="D"), B=date_range("2012-1-2", periods=3, freq="D"), C=Timestamp("20120101") - timedelta(minutes=5, seconds=5), ) ) diffs = DataFrame(dict(A=df["A"] - df["C"], B=df["A"] - df["B"])) # min result = diffs.min() assert result[0] == diffs.loc[0, "A"] assert result[1] == diffs.loc[0, "B"] result = diffs.min(axis=1) assert (result == diffs.loc[0, "B"]).all() # max result = diffs.max() assert result[0] == diffs.loc[2, "A"] assert result[1] == diffs.loc[2, "B"] result = diffs.max(axis=1) assert (result == diffs["A"]).all() # abs result = diffs.abs() result2 = abs(diffs) expected = DataFrame(dict(A=df["A"] - df["C"], B=df["B"] - df["A"])) tm.assert_frame_equal(result, expected) tm.assert_frame_equal(result2, expected) # mixed frame mixed = diffs.copy() mixed["C"] = "foo" mixed["D"] = 1 mixed["E"] = 1.0 mixed["F"] = Timestamp("20130101") # results in an object array result = mixed.min() expected = Series( [ pd.Timedelta(timedelta(seconds=5 * 60 + 5)), pd.Timedelta(timedelta(days=-1)), "foo", 1, 1.0, Timestamp("20130101"), ], index=mixed.columns, ) tm.assert_series_equal(result, expected) # excludes numeric result = mixed.min(axis=1) expected = Series([1, 1, 1.0], index=[0, 1, 2]) tm.assert_series_equal(result, expected) # works when only those columns are selected result = mixed[["A", "B"]].min(1) expected = Series([timedelta(days=-1)] * 3) tm.assert_series_equal(result, expected) result = mixed[["A", "B"]].min() expected = Series( [timedelta(seconds=5 * 60 + 5), timedelta(days=-1)], index=["A", "B"] ) tm.assert_series_equal(result, expected) # GH 3106 df = DataFrame( { "time": date_range("20130102", periods=5), "time2": date_range("20130105", periods=5), } ) df["off1"] = df["time2"] - df["time"] assert df["off1"].dtype == "timedelta64[ns]" df["off2"] = df["time"] - df["time2"] df._consolidate_inplace() assert df["off1"].dtype == "timedelta64[ns]" assert df["off2"].dtype == "timedelta64[ns]" def test_sum_corner(self): empty_frame = DataFrame() axis0 = empty_frame.sum(0) axis1 = empty_frame.sum(1) assert isinstance(axis0, Series) assert isinstance(axis1, Series) assert len(axis0) == 0 assert len(axis1) == 0 @pytest.mark.parametrize("method, unit", [("sum", 0), ("prod", 1)]) def test_sum_prod_nanops(self, method, unit): idx = ["a", "b", "c"] df = pd.DataFrame( {"a": [unit, unit], "b": [unit, np.nan], "c": [np.nan, np.nan]} ) # The default result = getattr(df, method) expected = pd.Series([unit, unit, unit], index=idx, dtype="float64") # min_count=1 result = getattr(df, method)(min_count=1) expected = pd.Series([unit, unit, np.nan], index=idx) tm.assert_series_equal(result, expected) # min_count=0 result = getattr(df, method)(min_count=0) expected = pd.Series([unit, unit, unit], index=idx, dtype="float64") tm.assert_series_equal(result, expected) result = getattr(df.iloc[1:], method)(min_count=1) expected = pd.Series([unit, np.nan, np.nan], index=idx) tm.assert_series_equal(result, expected) # min_count > 1 df = pd.DataFrame({"A": [unit] * 10, "B": [unit] * 5 + [np.nan] * 5}) result = getattr(df, method)(min_count=5) expected = pd.Series(result, index=["A", "B"]) tm.assert_series_equal(result, expected) result = getattr(df, method)(min_count=6) expected = pd.Series(result, index=["A", "B"]) tm.assert_series_equal(result, expected) def test_sum_nanops_timedelta(self): # prod isn't defined on timedeltas idx = ["a", "b", "c"] df = pd.DataFrame({"a": [0, 0], "b": [0, np.nan], "c": [np.nan, np.nan]}) df2 = df.apply(pd.to_timedelta) # 0 by default result = df2.sum() expected = pd.Series([0, 0, 0], dtype="m8[ns]", index=idx) tm.assert_series_equal(result, expected) # min_count=0 result = df2.sum(min_count=0) tm.assert_series_equal(result, expected) # min_count=1 result = df2.sum(min_count=1) expected = pd.Series([0, 0, np.nan], dtype="m8[ns]", index=idx) tm.assert_series_equal(result, expected) def test_sum_object(self, float_frame): values = float_frame.values.astype(int) frame = DataFrame(values, index=float_frame.index, columns=float_frame.columns) deltas = frame * timedelta(1) deltas.sum() def test_sum_bool(self, float_frame): # ensure this works, bug report bools = np.isnan(float_frame) bools.sum(1) bools.sum(0) def test_sum_mixed_datetime(self): # GH#30886 df = pd.DataFrame( {"A": pd.date_range("2000", periods=4), "B": [1, 2, 3, 4]} ).reindex([2, 3, 4]) result = df.sum() expected = pd.Series({"B": 7.0}) tm.assert_series_equal(result, expected) def test_mean_corner(self, float_frame, float_string_frame): # unit test when have object data the_mean = float_string_frame.mean(axis=0) the_sum = float_string_frame.sum(axis=0, numeric_only=True) tm.assert_index_equal(the_sum.index, the_mean.index) assert len(the_mean.index) < len(float_string_frame.columns) # xs sum mixed type, just want to know it works... the_mean = float_string_frame.mean(axis=1) the_sum = float_string_frame.sum(axis=1, numeric_only=True) tm.assert_index_equal(the_sum.index, the_mean.index) # take mean of boolean column float_frame["bool"] = float_frame["A"] > 0 means = float_frame.mean(0) assert means["bool"] == float_frame["bool"].values.mean() def test_mean_datetimelike(self): # GH#24757 check that datetimelike are excluded by default, handled # correctly with numeric_only=True df = pd.DataFrame( { "A": np.arange(3), "B": pd.date_range("2016-01-01", periods=3), "C": pd.timedelta_range("1D", periods=3), "D": pd.period_range("2016", periods=3, freq="A"), } ) result = df.mean(numeric_only=True) expected = pd.Series({"A": 1.0}) tm.assert_series_equal(result, expected) with tm.assert_produces_warning(FutureWarning): # in the future datetime columns will be included result = df.mean() expected = pd.Series({"A": 1.0, "C": df.loc[1, "C"]}) tm.assert_series_equal(result, expected) def test_mean_datetimelike_numeric_only_false(self): df = pd.DataFrame( { "A": np.arange(3), "B": pd.date_range("2016-01-01", periods=3), "C": pd.timedelta_range("1D", periods=3), } ) # datetime(tz) and timedelta work result = df.mean(numeric_only=False) expected = pd.Series({"A": 1, "B": df.loc[1, "B"], "C": df.loc[1, "C"]}) tm.assert_series_equal(result, expected) # mean of period is not allowed df["D"] = pd.period_range("2016", periods=3, freq="A") with pytest.raises(TypeError, match="mean is not implemented for Period"): df.mean(numeric_only=False) def test_mean_extensionarray_numeric_only_true(self): # https://github.com/pandas-dev/pandas/issues/33256 arr = np.random.randint(1000, size=(10, 5)) df = pd.DataFrame(arr, dtype="Int64") result = df.mean(numeric_only=True) expected = pd.DataFrame(arr).mean() tm.assert_series_equal(result, expected) def test_stats_mixed_type(self, float_string_frame): # don't blow up float_string_frame.std(1) float_string_frame.var(1) float_string_frame.mean(1) float_string_frame.skew(1) def test_sum_bools(self): df = DataFrame(index=range(1), columns=range(10)) bools = isna(df) assert bools.sum(axis=1)[0] == 10 # ---------------------------------------------------------------------- # Index of max / min def test_idxmin(self, float_frame, int_frame): frame = float_frame frame.iloc[5:10] = np.nan frame.iloc[15:20, -2:] = np.nan for skipna in [True, False]: for axis in [0, 1]: for df in [frame, int_frame]: result = df.idxmin(axis=axis, skipna=skipna) expected = df.apply(Series.idxmin, axis=axis, skipna=skipna) tm.assert_series_equal(result, expected) msg = "No axis named 2 for object type DataFrame" with pytest.raises(ValueError, match=msg): frame.idxmin(axis=2) def test_idxmax(self, float_frame, int_frame): frame = float_frame frame.iloc[5:10] = np.nan frame.iloc[15:20, -2:] = np.nan for skipna in [True, False]: for axis in [0, 1]: for df in [frame, int_frame]: result = df.idxmax(axis=axis, skipna=skipna) expected = df.apply(Series.idxmax, axis=axis, skipna=skipna) tm.assert_series_equal(result, expected) msg = "No axis named 2 for object type DataFrame" with pytest.raises(ValueError, match=msg): frame.idxmax(axis=2) # ---------------------------------------------------------------------- # Logical reductions @pytest.mark.parametrize("opname", ["any", "all"]) def test_any_all(self, opname, bool_frame_with_na, float_string_frame): assert_bool_op_calc( opname, getattr(np, opname), bool_frame_with_na, has_skipna=True ) assert_bool_op_api( opname, bool_frame_with_na, float_string_frame, has_bool_only=True ) def test_any_all_extra(self): df = DataFrame( { "A": [True, False, False], "B": [True, True, False], "C": [True, True, True], }, index=["a", "b", "c"], ) result = df[["A", "B"]].any(1) expected = Series([True, True, False], index=["a", "b", "c"]) tm.assert_series_equal(result, expected) result = df[["A", "B"]].any(1, bool_only=True) tm.assert_series_equal(result, expected) result = df.all(1) expected = Series([True, False, False], index=["a", "b", "c"]) tm.assert_series_equal(result, expected) result = df.all(1, bool_only=True) tm.assert_series_equal(result, expected) # Axis is None result = df.all(axis=None).item() assert result is False result = df.any(axis=None).item() assert result is True result = df[["C"]].all(axis=None).item() assert result is True def test_any_datetime(self): # GH 23070 float_data = [1, np.nan, 3, np.nan] datetime_data = [ pd.Timestamp("1960-02-15"), pd.Timestamp("1960-02-16"), pd.NaT, pd.NaT, ] df = DataFrame({"A": float_data, "B": datetime_data}) result = df.any(1) expected = Series([True, True, True, False]) tm.assert_series_equal(result, expected) def test_any_all_bool_only(self): # GH 25101 df = DataFrame( {"col1": [1, 2, 3], "col2": [4, 5, 6], "col3": [None, None, None]} ) result = df.all(bool_only=True) expected = Series(dtype=np.bool_) tm.assert_series_equal(result, expected) df = DataFrame( { "col1": [1, 2, 3], "col2": [4, 5, 6], "col3": [None, None, None], "col4": [False, False, True], } ) result = df.all(bool_only=True) expected = Series({"col4": False}) tm.assert_series_equal(result, expected) @pytest.mark.parametrize( "func, data, expected", [ (np.any, {}, False), (np.all, {}, True), (np.any, {"A": []}, False), (np.all, {"A": []}, True), (np.any, {"A": [False, False]}, False), (np.all, {"A": [False, False]}, False), (np.any, {"A": [True, False]}, True), (np.all, {"A": [True, False]}, False), (np.any, {"A": [True, True]}, True), (np.all, {"A": [True, True]}, True), (np.any, {"A": [False], "B": [False]}, False), (np.all, {"A": [False], "B": [False]}, False), (np.any, {"A": [False, False], "B": [False, True]}, True), (np.all, {"A": [False, False], "B": [False, True]}, False), # other types (np.all, {"A": pd.Series([0.0, 1.0], dtype="float")}, False), (np.any, {"A": pd.Series([0.0, 1.0], dtype="float")}, True), (np.all, {"A": pd.Series([0, 1], dtype=int)}, False), (np.any, {"A": pd.Series([0, 1], dtype=int)}, True), pytest.param( np.all, {"A": pd.Series([0, 1], dtype="M8[ns]")}, False, marks=[td.skip_if_np_lt("1.15")], ), pytest.param( np.any, {"A": pd.Series([0, 1], dtype="M8[ns]")}, True, marks=[td.skip_if_np_lt("1.15")], ), pytest.param( np.all, {"A": pd.Series([1, 2], dtype="M8[ns]")}, True, marks=[td.skip_if_np_lt("1.15")], ), pytest.param( np.any, {"A": pd.Series([1, 2], dtype="M8[ns]")}, True, marks=[td.skip_if_np_lt("1.15")], ), pytest.param( np.all, {"A": pd.Series([0, 1], dtype="m8[ns]")}, False, marks=[td.skip_if_np_lt("1.15")], ), pytest.param( np.any, {"A": pd.Series([0, 1], dtype="m8[ns]")}, True, marks=[td.skip_if_np_lt("1.15")], ), pytest.param( np.all, {"A": pd.Series([1, 2], dtype="m8[ns]")}, True, marks=[td.skip_if_np_lt("1.15")], ), pytest.param( np.any, {"A": pd.Series([1, 2], dtype="m8[ns]")}, True, marks=[td.skip_if_np_lt("1.15")], ), (np.all, {"A": pd.Series([0, 1], dtype="category")}, False), (np.any, {"A": pd.Series([0, 1], dtype="category")}, True), (np.all, {"A": pd.Series([1, 2], dtype="category")}, True), (np.any, {"A": pd.Series([1, 2], dtype="category")}, True), # Mix GH#21484 pytest.param( np.all, { "A": pd.Series([10, 20], dtype="M8[ns]"), "B": pd.Series([10, 20], dtype="m8[ns]"), }, True, # In 1.13.3 and 1.14 np.all(df) returns a Timedelta here marks=[td.skip_if_np_lt("1.15")], ), ], ) def test_any_all_np_func(self, func, data, expected): # GH 19976 data = DataFrame(data) result = func(data) assert isinstance(result, np.bool_) assert result.item() is expected # method version result = getattr(DataFrame(data), func.__name__)(axis=None) assert isinstance(result, np.bool_) assert result.item() is expected def test_any_all_object(self): # GH 19976 result = np.all(DataFrame(columns=["a", "b"])).item() assert result is True result = np.any(DataFrame(columns=["a", "b"])).item() assert result is False @pytest.mark.parametrize("method", ["any", "all"]) def test_any_all_level_axis_none_raises(self, method): df = DataFrame( {"A": 1}, index=MultiIndex.from_product( [["A", "B"], ["a", "b"]], names=["out", "in"] ), ) xpr = "Must specify 'axis' when aggregating by level." with pytest.raises(ValueError, match=xpr): getattr(df, method)(axis=None, level="out") # --------------------------------------------------------------------- # Matrix-like def test_matmul(self): # matmul test is for GH 10259 a = DataFrame( np.random.randn(3, 4), index=["a", "b", "c"], columns=["p", "q", "r", "s"] ) b = DataFrame( np.random.randn(4, 2), index=["p", "q", "r", "s"], columns=["one", "two"] ) # DataFrame @ DataFrame result = operator.matmul(a, b) expected = DataFrame( np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"] ) tm.assert_frame_equal(result, expected) # DataFrame @ Series result = operator.matmul(a, b.one) expected = Series(np.dot(a.values, b.one.values), index=["a", "b", "c"]) tm.assert_series_equal(result, expected) # np.array @ DataFrame result = operator.matmul(a.values, b) assert isinstance(result, DataFrame) assert result.columns.equals(b.columns) assert result.index.equals(pd.Index(range(3))) expected = np.dot(a.values, b.values) tm.assert_almost_equal(result.values, expected) # nested list @ DataFrame (__rmatmul__) result = operator.matmul(a.values.tolist(), b) expected = DataFrame( np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"] ) tm.assert_almost_equal(result.values, expected.values) # mixed dtype DataFrame @ DataFrame a["q"] = a.q.round().astype(int) result = operator.matmul(a, b) expected = DataFrame( np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"] ) tm.assert_frame_equal(result, expected) # different dtypes DataFrame @ DataFrame a = a.astype(int) result = operator.matmul(a, b) expected = DataFrame( np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"] ) tm.assert_frame_equal(result, expected) # unaligned df = DataFrame(np.random.randn(3, 4), index=[1, 2, 3], columns=range(4)) df2 = DataFrame(np.random.randn(5, 3), index=range(5), columns=[1, 2, 3]) with pytest.raises(ValueError, match="aligned"): operator.matmul(df, df2) # --------------------------------------------------------------------- # Unsorted def test_series_broadcasting(self): # smoke test for numpy warnings # GH 16378, GH 16306 df = DataFrame([1.0, 1.0, 1.0]) df_nan = DataFrame({"A": [np.nan, 2.0, np.nan]}) s = Series([1, 1, 1]) s_nan = Series([np.nan, np.nan, 1]) with tm.assert_produces_warning(None): df_nan.clip(lower=s, axis=0) for op in ["lt", "le", "gt", "ge", "eq", "ne"]: getattr(df, op)(s_nan, axis=0) class TestDataFrameReductions: def test_min_max_dt64_with_NaT(self): # Both NaT and Timestamp are in DataFrame. df = pd.DataFrame({"foo": [pd.NaT, pd.NaT, pd.Timestamp("2012-05-01")]}) res = df.min() exp = pd.Series([pd.Timestamp("2012-05-01")], index=["foo"]) tm.assert_series_equal(res, exp) res = df.max() exp = pd.Series([pd.Timestamp("2012-05-01")], index=["foo"]) tm.assert_series_equal(res, exp) # GH12941, only NaTs are in DataFrame. df = pd.DataFrame({"foo": [pd.NaT, pd.NaT]}) res = df.min() exp = pd.Series([pd.NaT], index=["foo"]) tm.assert_series_equal(res, exp) res = df.max() exp = pd.Series([pd.NaT], index=["foo"]) tm.assert_series_equal(res, exp) def test_min_max_dt64_api_consistency_with_NaT(self): # Calling the following sum functions returned an error for dataframes but # returned NaT for series. These tests check that the API is consistent in # min/max calls on empty Series/DataFrames. See GH:33704 for more # information df = pd.DataFrame(dict(x=pd.to_datetime([]))) expected_dt_series = pd.Series(pd.to_datetime([])) # check axis 0 assert (df.min(axis=0).x is pd.NaT) == (expected_dt_series.min() is pd.NaT) assert (df.max(axis=0).x is pd.NaT) == (expected_dt_series.max() is pd.NaT) # check axis 1 tm.assert_series_equal(df.min(axis=1), expected_dt_series) tm.assert_series_equal(df.max(axis=1), expected_dt_series) def test_min_max_dt64_api_consistency_empty_df(self): # check DataFrame/Series api consistency when calling min/max on an empty # DataFrame/Series. df = pd.DataFrame(dict(x=[])) expected_float_series = pd.Series([], dtype=float) # check axis 0 assert np.isnan(df.min(axis=0).x) == np.isnan(expected_float_series.min()) assert np.isnan(df.max(axis=0).x) == np.isnan(expected_float_series.max()) # check axis 1 tm.assert_series_equal(df.min(axis=1), expected_float_series) tm.assert_series_equal(df.min(axis=1), expected_float_series) @pytest.mark.parametrize( "initial", ["2018-10-08 13:36:45+00:00", "2018-10-08 13:36:45+03:00"], # Non-UTC timezone ) @pytest.mark.parametrize("method", ["min", "max"]) def test_preserve_timezone(self, initial: str, method): # GH 28552 initial_dt = pd.to_datetime(initial) expected = Series([initial_dt]) df = DataFrame([expected]) result = getattr(df, method)(axis=1) tm.assert_series_equal(result, expected) def test_mixed_frame_with_integer_sum(): # https://github.com/pandas-dev/pandas/issues/34520 df = pd.DataFrame([["a", 1]], columns=list("ab")) df = df.astype({"b": "Int64"}) result = df.sum() expected = pd.Series(["a", 1], index=["a", "b"]) tm.assert_series_equal(result, expected) @pytest.mark.parametrize("numeric_only", [True, False, None]) @pytest.mark.parametrize("method", ["min", "max"]) def test_minmax_extensionarray(method, numeric_only): # https://github.com/pandas-dev/pandas/issues/32651 int64_info = np.iinfo("int64") ser = Series([int64_info.max, None, int64_info.min], dtype=pd.Int64Dtype()) df = DataFrame({"Int64": ser}) result = getattr(df, method)(numeric_only=numeric_only) expected = Series( [getattr(int64_info, method)], index=pd.Index(["Int64"], dtype="object") ) tm.assert_series_equal(result, expected)