def test_transform_listlike(axis, float_frame, ops, names):
# GH 35964
other_axis = 1 if axis in {0, "index"} else 0
with np.errstate(all="ignore"):
expected = zip_frames([op(float_frame) for op in ops], axis=other_axis)
if axis in {0, "index"}:
expected.columns = MultiIndex.from_product([float_frame.columns, names])
else:
expected.index = MultiIndex.from_product([float_frame.index, names])
result = float_frame.transform(ops, axis=axis)
tm.assert_frame_equal(result, expected)
def test_agg_transform(axis, float_frame):
other_axis = 1 if axis in {0, "index"} else 0
with np.errstate(all="ignore"):
f_abs = np.abs(float_frame)
f_sqrt = np.sqrt(float_frame)
# ufunc
result = float_frame.transform(np.sqrt, axis=axis)
expected = f_sqrt.copy()
tm.assert_frame_equal(result, expected)
result = float_frame.transform(np.sqrt, axis=axis)
tm.assert_frame_equal(result, expected)
# list-like
expected = f_sqrt.copy()
if axis in {0, "index"}:
expected.columns = pd.MultiIndex.from_product(
[float_frame.columns, ["sqrt"]])
else:
expected.index = pd.MultiIndex.from_product(
[float_frame.index, ["sqrt"]])
result = float_frame.transform([np.sqrt], axis=axis)
tm.assert_frame_equal(result, expected)
# multiple items in list
# these are in the order as if we are applying both
# functions per series and then concatting
expected = zip_frames([f_abs, f_sqrt], axis=other_axis)
if axis in {0, "index"}:
expected.columns = pd.MultiIndex.from_product(
[float_frame.columns, ["absolute", "sqrt"]])
else:
expected.index = pd.MultiIndex.from_product(
[float_frame.index, ["absolute", "sqrt"]])
result = float_frame.transform([np.abs, "sqrt"], axis=axis)
tm.assert_frame_equal(result, expected)
