def test_pandas_groupbynan_regular_nanback(self):
df = pandas.DataFrame([dict(a="a", b=1, cc=0), dict(a="a", b=2)])
gr = df.groupby(["a", "cc"]).sum()
self.assertEqual(len(gr), 1)
self.assertRaise(
lambda: pandas_groupby_nan(df, ["a", "cc"], nanback=True).sum(),
NotImplementedError)
def test_pandas_groupbynan_tuple(self):
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")
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_doc3(self):
data = [
dict(a=2, ind="a", n=1),
dict(a=2, ind="a"),
dict(a=3, ind="b"),
dict(a=30)
]
df = pandas.DataFrame(data)
self.assertRaise(lambda: pandas_groupby_nan(df, ["ind", "n"]).sum(),
NotImplementedError)
def test_pandas_groupbynan_doc2(self):
data = [
dict(a=2, ind="a", n=1),
dict(a=2, ind="a"),
dict(a=3, ind="b"),
dict(a=30)
]
df = pandas.DataFrame(data)
gr2 = pandas_groupby_nan(df, ["ind", "a"], nanback=False).sum()
ind = list(gr2['ind'])
self.assertEqual(ind[-1], "²nan")
def test_pandas_groupbynan_doc(self):
data = [
dict(a=2, ind="a", n=1),
dict(a=2, ind="a"),
dict(a=3, ind="b"),
dict(a=30)
]
df = pandas.DataFrame(data)
gr2 = pandas_groupby_nan(df, ["ind"]).sum()
ind = list(gr2['ind'])
self.assertTrue(numpy.isnan(ind[-1]))
val = list(gr2['a'])
self.assertEqual(val[-1], 30)
def test_pandas_groupbynan(self):
self.assertTrue(sparse_lsqr is not None)
types = [(int, -10), (float, -20.2), (str, "e"),
(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"])
try:
self.assertIsInstance(li[-1], float)
except AssertionError as e:
raise AssertionError("Issue with {0}".format(ty)) from e
try:
self.assertTrue(numpy.isnan(li[-1]))
except AssertionError as e:
raise AssertionError(
"Issue with value {0}\n--df--\n{1}\n--co--\n{2}".format(
li, df, co)) from e
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"])
self.assertIsInstance(li[-1], float)
self.assertTrue(numpy.isnan(li[-1]))
try:
gr = pandas_groupby_nan(df, ["value", "this"])
t = True
except (TypeError, NotImplementedError):
t = False
if t:
co = gr.sum()
li = list(co["value"])
self.assertEqual(len(li), 2)
def test_pandas_groupbynan_regular(self):
df = pandas.DataFrame([dict(a="a", b=1), dict(a="a", b=2)])
gr = df.groupby(["a"]).sum()
gr2_ = pandas_groupby_nan(df, ["a"]).sum()
self.assertEqualDataFrame(gr, gr2_)