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from itertools import chain
import operator
import numpy as np
import pytest
from pandas.core.dtypes.common import is_number
from pandas import (
DataFrame,
Series,
)
import pandas._testing as tm
from pandas.tests.apply.common import (
frame_transform_kernels,
series_transform_kernels,
)
@pytest.mark.parametrize("func", ["sum", "mean", "min", "max", "std"])
@pytest.mark.parametrize(
"args,kwds",
[
pytest.param([], {}, id="no_args_or_kwds"),
pytest.param([1], {}, id="axis_from_args"),
pytest.param([], {"axis": 1}, id="axis_from_kwds"),
pytest.param([], {"numeric_only": True}, id="optional_kwds"),
pytest.param([1, True], {"numeric_only": True}, id="args_and_kwds"),
],
)
@pytest.mark.parametrize("how", ["agg", "apply"])
def test_apply_with_string_funcs(request, float_frame, func, args, kwds, how):
if len(args) > 1 and how == "agg":
request.applymarker(
pytest.mark.xfail(
raises=TypeError,
reason="agg/apply signature mismatch - agg passes 2nd "
"argument to func",
)
)
result = getattr(float_frame, how)(func, *args, **kwds)
expected = getattr(float_frame, func)(*args, **kwds)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("arg", ["sum", "mean", "min", "max", "std"])
def test_with_string_args(datetime_series, arg):
result = datetime_series.apply(arg)
expected = getattr(datetime_series, arg)()
assert result == expected
@pytest.mark.parametrize("op", ["mean", "median", "std", "var"])
@pytest.mark.parametrize("how", ["agg", "apply"])
def test_apply_np_reducer(op, how):
# GH 39116
float_frame = DataFrame({"a": [1, 2], "b": [3, 4]})
result = getattr(float_frame, how)(op)
# pandas ddof defaults to 1, numpy to 0
kwargs = {"ddof": 1} if op in ("std", "var") else {}
expected = Series(
getattr(np, op)(float_frame, axis=0, **kwargs), index=float_frame.columns
)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"op", ["abs", "ceil", "cos", "cumsum", "exp", "log", "sqrt", "square"]
)
@pytest.mark.parametrize("how", ["transform", "apply"])
def test_apply_np_transformer(float_frame, op, how):
# GH 39116
# float_frame will _usually_ have negative values, which will
# trigger the warning here, but let's put one in just to be sure
float_frame.iloc[0, 0] = -1.0
warn = None
if op in ["log", "sqrt"]:
warn = RuntimeWarning
with tm.assert_produces_warning(warn, check_stacklevel=False):
# float_frame fixture is defined in conftest.py, so we don't check the
# stacklevel as otherwise the test would fail.
result = getattr(float_frame, how)(op)
expected = getattr(np, op)(float_frame)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"series, func, expected",
chain(
tm.get_cython_table_params(
Series(dtype=np.float64),
[
("sum", 0),
("max", np.nan),
("min", np.nan),
("all", True),
("any", False),
("mean", np.nan),
("prod", 1),
("std", np.nan),
("var", np.nan),
("median", np.nan),
],
),
tm.get_cython_table_params(
Series([np.nan, 1, 2, 3]),
[
("sum", 6),
("max", 3),
("min", 1),
("all", True),
("any", True),
("mean", 2),
("prod", 6),
("std", 1),
("var", 1),
("median", 2),
],
),
tm.get_cython_table_params(
Series("a b c".split()),
[
("sum", "abc"),
("max", "c"),
("min", "a"),
("all", True),
("any", True),
],
),
),
)
def test_agg_cython_table_series(series, func, expected):
# GH21224
# test reducing functions in
# pandas.core.base.SelectionMixin._cython_table
warn = None if isinstance(func, str) else FutureWarning
with tm.assert_produces_warning(warn, match="is currently using Series.*"):
result = series.agg(func)
if is_number(expected):
assert np.isclose(result, expected, equal_nan=True)
else:
assert result == expected
@pytest.mark.parametrize(
"series, func, expected",
chain(
tm.get_cython_table_params(
Series(dtype=np.float64),
[
("cumprod", Series([], dtype=np.float64)),
("cumsum", Series([], dtype=np.float64)),
],
),
tm.get_cython_table_params(
Series([np.nan, 1, 2, 3]),
[
("cumprod", Series([np.nan, 1, 2, 6])),
("cumsum", Series([np.nan, 1, 3, 6])),
],
),
tm.get_cython_table_params(
Series("a b c".split()), [("cumsum", Series(["a", "ab", "abc"]))]
),
),
)
def test_agg_cython_table_transform_series(series, func, expected):
# GH21224
# test transforming functions in
# pandas.core.base.SelectionMixin._cython_table (cumprod, cumsum)
warn = None if isinstance(func, str) else FutureWarning
with tm.assert_produces_warning(warn, match="is currently using Series.*"):
result = series.agg(func)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"df, func, expected",
chain(
tm.get_cython_table_params(
DataFrame(),
[
("sum", Series(dtype="float64")),
("max", Series(dtype="float64")),
("min", Series(dtype="float64")),
("all", Series(dtype=bool)),
("any", Series(dtype=bool)),
("mean", Series(dtype="float64")),
("prod", Series(dtype="float64")),
("std", Series(dtype="float64")),
("var", Series(dtype="float64")),
("median", Series(dtype="float64")),
],
),
tm.get_cython_table_params(
DataFrame([[np.nan, 1], [1, 2]]),
[
("sum", Series([1.0, 3])),
("max", Series([1.0, 2])),
("min", Series([1.0, 1])),
("all", Series([True, True])),
("any", Series([True, True])),
("mean", Series([1, 1.5])),
("prod", Series([1.0, 2])),
("std", Series([np.nan, 0.707107])),
("var", Series([np.nan, 0.5])),
("median", Series([1, 1.5])),
],
),
),
)
def test_agg_cython_table_frame(df, func, expected, axis):
# GH 21224
# test reducing functions in
# pandas.core.base.SelectionMixin._cython_table
warn = None if isinstance(func, str) else FutureWarning
with tm.assert_produces_warning(warn, match="is currently using DataFrame.*"):
# GH#53425
result = df.agg(func, axis=axis)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"df, func, expected",
chain(
tm.get_cython_table_params(
DataFrame(), [("cumprod", DataFrame()), ("cumsum", DataFrame())]
),
tm.get_cython_table_params(
DataFrame([[np.nan, 1], [1, 2]]),
[
("cumprod", DataFrame([[np.nan, 1], [1, 2]])),
("cumsum", DataFrame([[np.nan, 1], [1, 3]])),
],
),
),
)
def test_agg_cython_table_transform_frame(df, func, expected, axis):
# GH 21224
# test transforming functions in
# pandas.core.base.SelectionMixin._cython_table (cumprod, cumsum)
if axis in ("columns", 1):
# operating blockwise doesn't let us preserve dtypes
expected = expected.astype("float64")
warn = None if isinstance(func, str) else FutureWarning
with tm.assert_produces_warning(warn, match="is currently using DataFrame.*"):
# GH#53425
result = df.agg(func, axis=axis)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("op", series_transform_kernels)
def test_transform_groupby_kernel_series(request, string_series, op):
# GH 35964
if op == "ngroup":
request.applymarker(
pytest.mark.xfail(raises=ValueError, reason="ngroup not valid for NDFrame")
)
args = [0.0] if op == "fillna" else []
ones = np.ones(string_series.shape[0])
warn = FutureWarning if op == "fillna" else None
msg = "SeriesGroupBy.fillna is deprecated"
with tm.assert_produces_warning(warn, match=msg):
expected = string_series.groupby(ones).transform(op, *args)
result = string_series.transform(op, 0, *args)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("op", frame_transform_kernels)
def test_transform_groupby_kernel_frame(request, axis, float_frame, op):
if op == "ngroup":
request.applymarker(
pytest.mark.xfail(raises=ValueError, reason="ngroup not valid for NDFrame")
)
# GH 35964
args = [0.0] if op == "fillna" else []
if axis in (0, "index"):
ones = np.ones(float_frame.shape[0])
msg = "The 'axis' keyword in DataFrame.groupby is deprecated"
else:
ones = np.ones(float_frame.shape[1])
msg = "DataFrame.groupby with axis=1 is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
gb = float_frame.groupby(ones, axis=axis)
warn = FutureWarning if op == "fillna" else None
op_msg = "DataFrameGroupBy.fillna is deprecated"
with tm.assert_produces_warning(warn, match=op_msg):
expected = gb.transform(op, *args)
result = float_frame.transform(op, axis, *args)
tm.assert_frame_equal(result, expected)
# same thing, but ensuring we have multiple blocks
assert "E" not in float_frame.columns
float_frame["E"] = float_frame["A"].copy()
assert len(float_frame._mgr.arrays) > 1
if axis in (0, "index"):
ones = np.ones(float_frame.shape[0])
else:
ones = np.ones(float_frame.shape[1])
with tm.assert_produces_warning(FutureWarning, match=msg):
gb2 = float_frame.groupby(ones, axis=axis)
warn = FutureWarning if op == "fillna" else None
op_msg = "DataFrameGroupBy.fillna is deprecated"
with tm.assert_produces_warning(warn, match=op_msg):
expected2 = gb2.transform(op, *args)
result2 = float_frame.transform(op, axis, *args)
tm.assert_frame_equal(result2, expected2)
@pytest.mark.parametrize("method", ["abs", "shift", "pct_change", "cumsum", "rank"])
def test_transform_method_name(method):
# GH 19760
df = DataFrame({"A": [-1, 2]})
result = df.transform(method)
expected = operator.methodcaller(method)(df)
tm.assert_frame_equal(result, expected)
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