def test_pandas_groupbynan(self):
fLOG(
__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
types = [(str, "e"), (int, -10), (float, -20.2),
(bytes, bytes("a", "ascii"))]
skip = (numpy.bool_, numpy.complex64, numpy.complex128)
types += [(_, _(5)) for _ in numpy_types() if _ not in skip]
for ty in types:
data = [{"this": "cst", "type": "tt1=" + str(ty[0]), "value": ty[1]},
{"this": "cst", "type": "tt2=" +
str(ty[0]), "value": ty[1]},
{"this": "cst", "type": "row_for_nan"}]
df = pandas.DataFrame(data)
gr = pandas_groupby_nan(df, "value")
co = gr.sum()
li = list(co["value"])
# fLOG("###", li)
assert numpy.isnan(li[-1])
for ty in types:
data = [{"this": "cst", "type": "tt1=" + str(ty[0]), "value": ty[1]},
{"this": "cst", "type": "tt2=" +
str(ty[0]), "value": ty[1]},
{"this": "cst", "type": "row_for_nan"}]
df = pandas.DataFrame(data)
try:
gr = pandas_groupby_nan(df, ("value", "this"))
t = True
raise Exception("---")
except TypeError:
t = False
if t:
co = gr.sum()
li = list(co["value"])
# fLOG("###", li)
assert numpy.isnan(li[-1])
try:
gr = pandas_groupby_nan(df, ["value", "this"])
t = True
except (TypeError, NotImplementedError):
t = False
pass
if t:
co = gr.sum()
li = list(co["value"])
self.assertEqual(len(li), 2)
def test_pandas_groupbynan_tuple(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
data = [
dict(a="a", b="b", c="c", n=1),
dict(b="b", n=2),
dict(a="a", n=3),
dict(c="c", n=4)
]
df = pandas.DataFrame(data)
gr = df.groupby(["a", "b", "c"]).sum()
self.assertEqual(gr.shape, (1, 1))
for nanback in [True, False]:
try:
gr2_ = pandas_groupby_nan(df, ["a", "b", "c"],
nanback=nanback,
suffix="NAN")
except NotImplementedError:
continue
gr2 = gr2_.sum().sort_values("n")
self.assertEqual(gr2.shape, (4, 4))
d = gr2.to_dict("records")
fLOG(gr2)
self.assertEqual(d[0]["a"], "a")
self.assertEqual(d[0]["b"], "b")
self.assertEqual(d[0]["c"], "c")
self.assertEqual(d[0]["n"], 1)
self.assertEqual(d[1]["a"], "NAN")
def test_pandas_groupbynan_tuple(self):
fLOG(
__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
data = [dict(a="a", b="b", c="c", n=1), dict(
b="b", n=2), dict(a="a", n=3), dict(c="c", n=4)]
df = pandas.DataFrame(data)
gr = df.groupby(["a", "b", "c"]).sum()
self.assertEqual(gr.shape, (1, 1))
for nanback in [True, False]:
try:
gr2_ = pandas_groupby_nan(
df, ["a", "b", "c"], nanback=nanback, suffix="NAN")
except NotImplementedError:
continue
gr2 = gr2_.sum().sort_values("n")
self.assertEqual(gr2.shape, (4, 4))
d = gr2.to_dict("records")
fLOG(gr2)
self.assertEqual(d[0]["a"], "a")
self.assertEqual(d[0]["b"], "b")
self.assertEqual(d[0]["c"], "c")
self.assertEqual(d[0]["n"], 1)
self.assertEqual(d[1]["a"], "NAN")
def test_pandas_groupbynan(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
types = [(str, "e"), (int, -10), (float, -20.2),
(bytes, bytes("a", "ascii"))]
skip = (numpy.bool_, numpy.complex64, numpy.complex128)
types += [(_, _(5)) for _ in numpy_types() if _ not in skip]
for ty in types:
data = [{
"this": "cst",
"type": "tt1=" + str(ty[0]),
"value": ty[1]
}, {
"this": "cst",
"type": "tt2=" + str(ty[0]),
"value": ty[1]
}, {
"this": "cst",
"type": "row_for_nan"
}]
df = pandas.DataFrame(data)
gr = pandas_groupby_nan(df, "value")
co = gr.sum()
li = list(co["value"])
# fLOG("###", li)
assert numpy.isnan(li[-1])
for ty in types:
data = [{
"this": "cst",
"type": "tt1=" + str(ty[0]),
"value": ty[1]
}, {
"this": "cst",
"type": "tt2=" + str(ty[0]),
"value": ty[1]
}, {
"this": "cst",
"type": "row_for_nan"
}]
df = pandas.DataFrame(data)
try:
gr = pandas_groupby_nan(df, ("value", "this"))
t = True
raise Exception("---")
except TypeError:
t = False
if t:
co = gr.sum()
li = list(co["value"])
# fLOG("###", li)
assert numpy.isnan(li[-1])
try:
gr = pandas_groupby_nan(df, ["value", "this"])
t = True
except (TypeError, NotImplementedError):
t = False
pass
if t:
co = gr.sum()
li = list(co["value"])
self.assertEqual(len(li), 2)
