def test_metadata_propagation_indiv(self, monkeypatch):
# check that the metadata matches up on the resulting ops
ser = Series(range(3), range(3))
ser.name = "foo"
ser2 = Series(range(3), range(3))
ser2.name = "bar"
result = ser.T
tm.assert_metadata_equivalent(ser, result)
def finalize(self, other, method=None, **kwargs):
for name in self._metadata:
if method == "concat" and name == "filename":
value = "+".join([
getattr(obj, name) for obj in other.objs
if getattr(obj, name, None)
])
object.__setattr__(self, name, value)
else:
object.__setattr__(self, name, getattr(other, name, None))
return self
with monkeypatch.context() as m:
m.setattr(Series, "_metadata", ["name", "filename"])
m.setattr(Series, "__finalize__", finalize)
ser.filename = "foo"
ser2.filename = "bar"
result = pd.concat([ser, ser2])
assert result.filename == "foo+bar"
assert result.name is None
def test_metadata_propagation_indiv_resample(self):
# resample
df = DataFrame(
np.random.randn(1000, 2),
index=date_range("20130101", periods=1000, freq="s"),
)
result = df.resample("1T")
tm.assert_metadata_equivalent(df, result)
def test_metadata_propagation_indiv_groupby(self):
# groupby
df = DataFrame({
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
"B": ["one", "one", "two", "three", "two", "two", "one", "three"],
"C":
np.random.randn(8),
"D":
np.random.randn(8),
})
result = df.groupby("A").sum()
tm.assert_metadata_equivalent(df, result)
def test_metadata_propagation_indiv_groupby(self):
# groupby
df = DataFrame({
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
"B": ["one", "one", "two", "three", "two", "two", "one", "three"],
"C":
np.random.randn(8),
"D":
np.random.randn(8),
})
msg = "The default value of numeric_only"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = df.groupby("A").sum()
tm.assert_metadata_equivalent(df, result)
def test_metadata_propagation_indiv_resample(self):
# resample
ts = Series(
np.random.rand(1000),
index=date_range("20130101", periods=1000, freq="s"),
name="foo",
)
result = ts.resample("1T").mean()
tm.assert_metadata_equivalent(ts, result)
result = ts.resample("1T").min()
tm.assert_metadata_equivalent(ts, result)
result = ts.resample("1T").apply(lambda x: x.sum())
tm.assert_metadata_equivalent(ts, result)
def test_metadata_propagation(self, frame_or_series):
# check that the metadata matches up on the resulting ops
o = construct(frame_or_series, shape=3)
o.name = "foo"
o2 = construct(frame_or_series, shape=3)
o2.name = "bar"
# ----------
# preserving
# ----------
# simple ops with scalars
for op in ["__add__", "__sub__", "__truediv__", "__mul__"]:
result = getattr(o, op)(1)
tm.assert_metadata_equivalent(o, result)
# ops with like
for op in ["__add__", "__sub__", "__truediv__", "__mul__"]:
result = getattr(o, op)(o)
tm.assert_metadata_equivalent(o, result)
# simple boolean
for op in ["__eq__", "__le__", "__ge__"]:
v1 = getattr(o, op)(o)
tm.assert_metadata_equivalent(o, v1)
tm.assert_metadata_equivalent(o, v1 & v1)
tm.assert_metadata_equivalent(o, v1 | v1)
# combine_first
result = o.combine_first(o2)
tm.assert_metadata_equivalent(o, result)
# ---------------------------
# non-preserving (by default)
# ---------------------------
# add non-like
result = o + o2
tm.assert_metadata_equivalent(result)
# simple boolean
for op in ["__eq__", "__le__", "__ge__"]:
# this is a name matching op
v1 = getattr(o, op)(o)
v2 = getattr(o, op)(o2)
tm.assert_metadata_equivalent(v2)
tm.assert_metadata_equivalent(v1 & v2)
tm.assert_metadata_equivalent(v1 | v2)