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Dec 7, 2020
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TST: share transform tests #38304

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TST: share test_foo_transform tests
jbrockmendel Dec 5, 2020
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jbrockmendel Dec 5, 2020
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jbrockmendel Dec 5, 2020
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116 changes: 71 additions & 45 deletions pandas/tests/frame/apply/test_frame_transform.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,23 +10,41 @@
from pandas.core.groupby.base import transformation_kernels
from pandas.tests.frame.common import zip_frames


def test_transform_ufunc(axis, float_frame):
# tshift only works on time index and is deprecated
# There is no DataFrame.cumcount
frame_kernels = [
x for x in sorted(transformation_kernels) if x not in ["tshift", "cumcount"]
]


def unpack_obj(obj, klass, axis):
"""
Helper to ensure we have the right type of object for a test parametrized
over frame_or_series.
"""
if klass is not DataFrame:
obj = obj["A"]
if axis != 0:
pytest.skip(f"Test is only for DataFrame with axis={axis}")
return obj


def test_transform_ufunc(axis, float_frame, frame_or_series):
# GH 35964
obj = unpack_obj(float_frame, frame_or_series, axis)

with np.errstate(all="ignore"):
f_sqrt = np.sqrt(float_frame)
result = float_frame.transform(np.sqrt, axis=axis)
f_sqrt = np.sqrt(obj)

# ufunc
result = obj.transform(np.sqrt, axis=axis)
expected = f_sqrt
tm.assert_frame_equal(result, expected)
tm.assert_equal(result, expected)


@pytest.mark.parametrize("op", sorted(transformation_kernels))
@pytest.mark.parametrize("op", frame_kernels)
def test_transform_groupby_kernel(axis, float_frame, op):
# GH 35964
if op == "cumcount":
pytest.xfail("DataFrame.cumcount does not exist")
if op == "tshift":
pytest.xfail("Only works on time index and is deprecated")

args = [0.0] if op == "fillna" else []
if axis == 0 or axis == "index":
Expand Down Expand Up @@ -61,9 +79,11 @@ def test_transform_listlike(axis, float_frame, ops, names):


@pytest.mark.parametrize("ops", [[], np.array([])])
def test_transform_empty_listlike(float_frame, ops):
def test_transform_empty_listlike(float_frame, ops, frame_or_series):
obj = unpack_obj(float_frame, frame_or_series, 0)

with pytest.raises(ValueError, match="No transform functions were provided"):
float_frame.transform(ops)
obj.transform(ops)


@pytest.mark.parametrize("box", [dict, Series])
Expand All @@ -90,25 +110,29 @@ def test_transform_dictlike(axis, float_frame, box):
{"A": ["cumsum"], "B": []},
],
)
def test_transform_empty_dictlike(float_frame, ops):
def test_transform_empty_dictlike(float_frame, ops, frame_or_series):
obj = unpack_obj(float_frame, frame_or_series, 0)

with pytest.raises(ValueError, match="No transform functions were provided"):
float_frame.transform(ops)
obj.transform(ops)


@pytest.mark.parametrize("use_apply", [True, False])
def test_transform_udf(axis, float_frame, use_apply):
def test_transform_udf(axis, float_frame, use_apply, frame_or_series):
# GH 35964
obj = unpack_obj(float_frame, frame_or_series, axis)

# transform uses UDF either via apply or passing the entire DataFrame
def func(x):
# transform is using apply iff x is not a DataFrame
if use_apply == isinstance(x, DataFrame):
if use_apply == isinstance(x, frame_or_series):
# Force transform to fallback
raise ValueError
return x + 1

result = float_frame.transform(func, axis=axis)
expected = float_frame + 1
tm.assert_frame_equal(result, expected)
result = obj.transform(func, axis=axis)
expected = obj + 1
tm.assert_equal(result, expected)


@pytest.mark.parametrize("method", ["abs", "shift", "pct_change", "cumsum", "rank"])
Expand Down Expand Up @@ -142,54 +166,56 @@ def test_agg_dict_nested_renaming_depr():
df.transform({"A": {"foo": "min"}, "B": {"bar": "max"}})


def test_transform_reducer_raises(all_reductions):
def test_transform_reducer_raises(all_reductions, frame_or_series):
# GH 35964
op = all_reductions
df = DataFrame({"A": [1, 2, 3]})

obj = DataFrame({"A": [1, 2, 3]})
if frame_or_series is not DataFrame:
obj = obj["A"]

msg = "Function did not transform"
with pytest.raises(ValueError, match=msg):
df.transform(op)
obj.transform(op)
with pytest.raises(ValueError, match=msg):
df.transform([op])
obj.transform([op])
with pytest.raises(ValueError, match=msg):
df.transform({"A": op})
obj.transform({"A": op})
with pytest.raises(ValueError, match=msg):
df.transform({"A": [op]})
obj.transform({"A": [op]})


wont_fail = ["ffill", "bfill", "fillna", "pad", "backfill", "shift"]
frame_kernels_raise = [x for x in frame_kernels if x not in wont_fail]


# mypy doesn't allow adding lists of different types
# https://github.com/python/mypy/issues/5492
@pytest.mark.parametrize("op", [*sorted(transformation_kernels), lambda x: x + 1])
def test_transform_bad_dtype(op):
@pytest.mark.parametrize("op", [*frame_kernels_raise, lambda x: x + 1])
def test_transform_bad_dtype(op, frame_or_series):
# GH 35964
df = DataFrame({"A": 3 * [object]}) # DataFrame that will fail on most transforms
if op in ("backfill", "shift", "pad", "bfill", "ffill"):
pytest.xfail("Transform function works on any datatype")
obj = DataFrame({"A": 3 * [object]}) # DataFrame that will fail on most transforms
if frame_or_series is not DataFrame:
obj = obj["A"]

msg = "Transform function failed"

# tshift is deprecated
warn = None if op != "tshift" else FutureWarning
with tm.assert_produces_warning(warn, check_stacklevel=False):
with pytest.raises(ValueError, match=msg):
df.transform(op)
obj.transform(op)
with pytest.raises(ValueError, match=msg):
df.transform([op])
obj.transform([op])
with pytest.raises(ValueError, match=msg):
df.transform({"A": op})
obj.transform({"A": op})
with pytest.raises(ValueError, match=msg):
df.transform({"A": [op]})
obj.transform({"A": [op]})


@pytest.mark.parametrize("op", sorted(transformation_kernels))
@pytest.mark.parametrize("op", frame_kernels_raise)
def test_transform_partial_failure(op):
# GH 35964
wont_fail = ["ffill", "bfill", "fillna", "pad", "backfill", "shift"]
if op in wont_fail:
pytest.xfail("Transform kernel is successful on all dtypes")
if op == "cumcount":
pytest.xfail("transform('cumcount') not implemented")
if op == "tshift":
pytest.xfail("Only works on time index; deprecated")

# Using object makes most transform kernels fail
df = DataFrame({"A": 3 * [object], "B": [1, 2, 3]})
Expand All @@ -208,22 +234,22 @@ def test_transform_partial_failure(op):


@pytest.mark.parametrize("use_apply", [True, False])
def test_transform_passes_args(use_apply):
def test_transform_passes_args(use_apply, frame_or_series):
# GH 35964
# transform uses UDF either via apply or passing the entire DataFrame
expected_args = [1, 2]
expected_kwargs = {"c": 3}

def f(x, a, b, c):
# transform is using apply iff x is not a DataFrame
if use_apply == isinstance(x, DataFrame):
if use_apply == isinstance(x, frame_or_series):
# Force transform to fallback
raise ValueError
assert [a, b] == expected_args
assert c == expected_kwargs["c"]
return x

DataFrame([1]).transform(f, 0, *expected_args, **expected_kwargs)
frame_or_series([1]).transform(f, 0, *expected_args, **expected_kwargs)


def test_transform_missing_columns(axis):
Expand Down
124 changes: 6 additions & 118 deletions pandas/tests/series/apply/test_series_transform.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,25 +6,16 @@
from pandas.core.base import SpecificationError
from pandas.core.groupby.base import transformation_kernels


def test_transform_ufunc(string_series):
# GH 35964
with np.errstate(all="ignore"):
f_sqrt = np.sqrt(string_series)

# ufunc
result = string_series.transform(np.sqrt)
expected = f_sqrt.copy()
tm.assert_series_equal(result, expected)
# tshift only works on time index and is deprecated
# There is no Series.cumcount
series_kernels = [
x for x in sorted(transformation_kernels) if x not in ["tshift", "cumcount"]
]


@pytest.mark.parametrize("op", sorted(transformation_kernels))
@pytest.mark.parametrize("op", series_kernels)
def test_transform_groupby_kernel(string_series, op):
# GH 35964
if op == "cumcount":
pytest.xfail("Series.cumcount does not exist")
if op == "tshift":
pytest.xfail("Only works on time index and is deprecated")

args = [0.0] if op == "fillna" else []
ones = np.ones(string_series.shape[0])
Expand All @@ -51,12 +42,6 @@ def test_transform_listlike(string_series, ops, names):
tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize("ops", [[], np.array([])])
def test_transform_empty_listlike(string_series, ops):
with pytest.raises(ValueError, match="No transform functions were provided"):
string_series.transform(ops)


@pytest.mark.parametrize("box", [dict, Series])
def test_transform_dictlike(string_series, box):
# GH 35964
Expand All @@ -67,45 +52,6 @@ def test_transform_dictlike(string_series, box):
tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize(
"ops",
[
{},
{"A": []},
{"A": [], "B": ["cumsum"]},
{"A": ["cumsum"], "B": []},
{"A": [], "B": "cumsum"},
{"A": "cumsum", "B": []},
],
)
def test_transform_empty_dictlike(string_series, ops):
with pytest.raises(ValueError, match="No transform functions were provided"):
string_series.transform(ops)


def test_transform_udf(axis, string_series):
# GH 35964
# via apply
def func(x):
if isinstance(x, Series):
raise ValueError
return x + 1

result = string_series.transform(func)
expected = string_series + 1
tm.assert_series_equal(result, expected)

# via map Series -> Series
def func(x):
if not isinstance(x, Series):
raise ValueError
return x + 1

result = string_series.transform(func)
expected = string_series + 1
tm.assert_series_equal(result, expected)


def test_transform_wont_agg(string_series):
# GH 35964
# we are trying to transform with an aggregator
Expand All @@ -127,64 +73,6 @@ def test_transform_none_to_type():
df.transform({"a": int})


def test_transform_reducer_raises(all_reductions):
# GH 35964
op = all_reductions
s = Series([1, 2, 3])
msg = "Function did not transform"
with pytest.raises(ValueError, match=msg):
s.transform(op)
with pytest.raises(ValueError, match=msg):
s.transform([op])
with pytest.raises(ValueError, match=msg):
s.transform({"A": op})
with pytest.raises(ValueError, match=msg):
s.transform({"A": [op]})


# mypy doesn't allow adding lists of different types
# https://github.com/python/mypy/issues/5492
@pytest.mark.parametrize("op", [*sorted(transformation_kernels), lambda x: x + 1])
def test_transform_bad_dtype(op):
# GH 35964
s = Series(3 * [object]) # Series that will fail on most transforms
if op in ("backfill", "shift", "pad", "bfill", "ffill"):
pytest.xfail("Transform function works on any datatype")

msg = "Transform function failed"

# tshift is deprecated
warn = None if op != "tshift" else FutureWarning
with tm.assert_produces_warning(warn, check_stacklevel=False):
with pytest.raises(ValueError, match=msg):
s.transform(op)
with pytest.raises(ValueError, match=msg):
s.transform([op])
with pytest.raises(ValueError, match=msg):
s.transform({"A": op})
with pytest.raises(ValueError, match=msg):
s.transform({"A": [op]})


@pytest.mark.parametrize("use_apply", [True, False])
def test_transform_passes_args(use_apply):
# GH 35964
# transform uses UDF either via apply or passing the entire Series
expected_args = [1, 2]
expected_kwargs = {"c": 3}

def f(x, a, b, c):
# transform is using apply iff x is not a Series
if use_apply == isinstance(x, Series):
# Force transform to fallback
raise ValueError
assert [a, b] == expected_args
assert c == expected_kwargs["c"]
return x

Series([1]).transform(f, 0, *expected_args, **expected_kwargs)


def test_transform_axis_1_raises():
# GH 35964
msg = "No axis named 1 for object type Series"
Expand Down
23 changes: 0 additions & 23 deletions pandas/tests/series/conftest.py
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