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302 lines
9.6 KiB
302 lines
9.6 KiB
from itertools import chain
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import operator
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import numpy as np
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import pytest
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from pandas.core.dtypes.common import is_number
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from pandas import (
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DataFrame,
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Index,
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Series,
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)
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import pandas._testing as tm
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from pandas.core.groupby.base import maybe_normalize_deprecated_kernels
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from pandas.tests.apply.common import (
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frame_transform_kernels,
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series_transform_kernels,
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)
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@pytest.mark.parametrize("func", ["sum", "mean", "min", "max", "std"])
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@pytest.mark.parametrize(
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"args,kwds",
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[
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pytest.param([], {}, id="no_args_or_kwds"),
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pytest.param([1], {}, id="axis_from_args"),
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pytest.param([], {"axis": 1}, id="axis_from_kwds"),
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pytest.param([], {"numeric_only": True}, id="optional_kwds"),
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pytest.param([1, True], {"numeric_only": True}, id="args_and_kwds"),
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],
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)
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@pytest.mark.parametrize("how", ["agg", "apply"])
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def test_apply_with_string_funcs(request, float_frame, func, args, kwds, how):
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if len(args) > 1 and how == "agg":
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request.node.add_marker(
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pytest.mark.xfail(
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raises=TypeError,
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reason="agg/apply signature mismatch - agg passes 2nd "
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"argument to func",
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)
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)
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result = getattr(float_frame, how)(func, *args, **kwds)
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expected = getattr(float_frame, func)(*args, **kwds)
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tm.assert_series_equal(result, expected)
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def test_with_string_args(datetime_series):
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for arg in ["sum", "mean", "min", "max", "std"]:
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result = datetime_series.apply(arg)
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expected = getattr(datetime_series, arg)()
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assert result == expected
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@pytest.mark.parametrize("op", ["mean", "median", "std", "var"])
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@pytest.mark.parametrize("how", ["agg", "apply"])
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def test_apply_np_reducer(float_frame, op, how):
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# GH 39116
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float_frame = DataFrame({"a": [1, 2], "b": [3, 4]})
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result = getattr(float_frame, how)(op)
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# pandas ddof defaults to 1, numpy to 0
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kwargs = {"ddof": 1} if op in ("std", "var") else {}
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expected = Series(
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getattr(np, op)(float_frame, axis=0, **kwargs), index=float_frame.columns
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)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"op", ["abs", "ceil", "cos", "cumsum", "exp", "log", "sqrt", "square"]
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)
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@pytest.mark.parametrize("how", ["transform", "apply"])
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def test_apply_np_transformer(float_frame, op, how):
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# GH 39116
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# float_frame will _usually_ have negative values, which will
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# trigger the warning here, but let's put one in just to be sure
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float_frame.iloc[0, 0] = -1.0
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warn = None
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if op in ["log", "sqrt"]:
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warn = RuntimeWarning
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with tm.assert_produces_warning(warn):
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result = getattr(float_frame, how)(op)
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expected = getattr(np, op)(float_frame)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"series, func, expected",
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chain(
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tm.get_cython_table_params(
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Series(dtype=np.float64),
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[
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("sum", 0),
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("max", np.nan),
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("min", np.nan),
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("all", True),
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("any", False),
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("mean", np.nan),
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("prod", 1),
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("std", np.nan),
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("var", np.nan),
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("median", np.nan),
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],
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),
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tm.get_cython_table_params(
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Series([np.nan, 1, 2, 3]),
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[
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("sum", 6),
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("max", 3),
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("min", 1),
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("all", True),
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("any", True),
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("mean", 2),
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("prod", 6),
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("std", 1),
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("var", 1),
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("median", 2),
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],
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),
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tm.get_cython_table_params(
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Series("a b c".split()),
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[
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("sum", "abc"),
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("max", "c"),
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("min", "a"),
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("all", True),
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("any", True),
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],
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),
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),
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)
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def test_agg_cython_table_series(series, func, expected):
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# GH21224
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# test reducing functions in
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# pandas.core.base.SelectionMixin._cython_table
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result = series.agg(func)
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if is_number(expected):
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assert np.isclose(result, expected, equal_nan=True)
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else:
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assert result == expected
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@pytest.mark.parametrize(
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"series, func, expected",
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chain(
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tm.get_cython_table_params(
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Series(dtype=np.float64),
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[
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("cumprod", Series([], Index([]), dtype=np.float64)),
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("cumsum", Series([], Index([]), dtype=np.float64)),
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],
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),
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tm.get_cython_table_params(
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Series([np.nan, 1, 2, 3]),
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[
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("cumprod", Series([np.nan, 1, 2, 6])),
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("cumsum", Series([np.nan, 1, 3, 6])),
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],
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),
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tm.get_cython_table_params(
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Series("a b c".split()), [("cumsum", Series(["a", "ab", "abc"]))]
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),
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),
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)
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def test_agg_cython_table_transform_series(series, func, expected):
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# GH21224
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# test transforming functions in
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# pandas.core.base.SelectionMixin._cython_table (cumprod, cumsum)
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result = series.agg(func)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"df, func, expected",
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chain(
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tm.get_cython_table_params(
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DataFrame(),
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[
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("sum", Series(dtype="float64")),
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("max", Series(dtype="float64")),
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("min", Series(dtype="float64")),
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("all", Series(dtype=bool)),
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("any", Series(dtype=bool)),
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("mean", Series(dtype="float64")),
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("prod", Series(dtype="float64")),
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("std", Series(dtype="float64")),
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("var", Series(dtype="float64")),
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("median", Series(dtype="float64")),
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],
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),
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tm.get_cython_table_params(
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DataFrame([[np.nan, 1], [1, 2]]),
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[
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("sum", Series([1.0, 3])),
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("max", Series([1.0, 2])),
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("min", Series([1.0, 1])),
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("all", Series([True, True])),
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("any", Series([True, True])),
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("mean", Series([1, 1.5])),
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("prod", Series([1.0, 2])),
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("std", Series([np.nan, 0.707107])),
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("var", Series([np.nan, 0.5])),
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("median", Series([1, 1.5])),
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],
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),
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),
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)
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def test_agg_cython_table_frame(df, func, expected, axis):
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# GH 21224
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# test reducing functions in
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# pandas.core.base.SelectionMixin._cython_table
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result = df.agg(func, axis=axis)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"df, func, expected",
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chain(
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tm.get_cython_table_params(
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DataFrame(), [("cumprod", DataFrame()), ("cumsum", DataFrame())]
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),
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tm.get_cython_table_params(
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DataFrame([[np.nan, 1], [1, 2]]),
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[
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("cumprod", DataFrame([[np.nan, 1], [1, 2]])),
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("cumsum", DataFrame([[np.nan, 1], [1, 3]])),
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],
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),
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),
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)
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def test_agg_cython_table_transform_frame(df, func, expected, axis):
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# GH 21224
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# test transforming functions in
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# pandas.core.base.SelectionMixin._cython_table (cumprod, cumsum)
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if axis == "columns" or axis == 1:
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# operating blockwise doesn't let us preserve dtypes
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expected = expected.astype("float64")
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result = df.agg(func, axis=axis)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("op", series_transform_kernels)
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def test_transform_groupby_kernel_series(string_series, op):
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# GH 35964
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# TODO(2.0) Remove after pad/backfill deprecation enforced
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op = maybe_normalize_deprecated_kernels(op)
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args = [0.0] if op == "fillna" else []
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ones = np.ones(string_series.shape[0])
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expected = string_series.groupby(ones).transform(op, *args)
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result = string_series.transform(op, 0, *args)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("op", frame_transform_kernels)
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def test_transform_groupby_kernel_frame(
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axis, float_frame, op, using_array_manager, request
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):
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# TODO(2.0) Remove after pad/backfill deprecation enforced
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op = maybe_normalize_deprecated_kernels(op)
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# GH 35964
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if using_array_manager and op == "pct_change" and axis in (1, "columns"):
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# TODO(ArrayManager) shift with axis=1
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request.node.add_marker(
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pytest.mark.xfail(
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reason="shift axis=1 not yet implemented for ArrayManager"
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)
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)
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args = [0.0] if op == "fillna" else []
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if axis == 0 or axis == "index":
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ones = np.ones(float_frame.shape[0])
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else:
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ones = np.ones(float_frame.shape[1])
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expected = float_frame.groupby(ones, axis=axis).transform(op, *args)
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result = float_frame.transform(op, axis, *args)
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tm.assert_frame_equal(result, expected)
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# same thing, but ensuring we have multiple blocks
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assert "E" not in float_frame.columns
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float_frame["E"] = float_frame["A"].copy()
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assert len(float_frame._mgr.arrays) > 1
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if axis == 0 or axis == "index":
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ones = np.ones(float_frame.shape[0])
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else:
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ones = np.ones(float_frame.shape[1])
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expected2 = float_frame.groupby(ones, axis=axis).transform(op, *args)
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result2 = float_frame.transform(op, axis, *args)
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tm.assert_frame_equal(result2, expected2)
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@pytest.mark.parametrize("method", ["abs", "shift", "pct_change", "cumsum", "rank"])
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def test_transform_method_name(method):
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# GH 19760
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df = DataFrame({"A": [-1, 2]})
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result = df.transform(method)
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expected = operator.methodcaller(method)(df)
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tm.assert_frame_equal(result, expected)
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