def test_df_equal(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
exp1 = pandas.DataFrame(
[dict(k1="a", k2="b", v=4, i=1),
dict(k1="b", k2="b", v=1, i=2)])
exp2 = pandas.DataFrame(
[dict(k1="a", k2="b", v=4, i=1),
dict(k1="b", k2="b", v=1, i=2)])
assert df_equal(exp1, exp2)
exp1 = pandas.DataFrame(
[dict(k1="a", k2="b", v=4, i=1),
dict(k1="b", k2="b", v=1, i=2)])
exp2 = pandas.DataFrame(
[dict(k1="a", k2="b", v=4, i=1),
dict(k1="b", k2="b", v=1, i=2)])
exp2 = exp2[["k2", "k1", "v", "i"]]
assert df_equal(exp1, exp2)
exp1 = pandas.DataFrame(
[dict(k1="a", k2="b", v=4, i=1),
dict(k1="b", k2="b", v=1, i=2)])
exp2 = pandas.DataFrame(
[dict(k1="a", k2="b", v=3, i=1),
dict(k1="b", k2="b", v=1, i=2)])
assert not df_equal(exp1, exp2)
exp1 = pandas.DataFrame(
[dict(k1="a", k2="b", v=4, i=1),
dict(k1="b", k2="b", v=1, i=2)])
exp2 = pandas.DataFrame([
dict(k1="a", k2="b", v=numpy.nan, i=1),
dict(k1="b", k2="b", v=1, i=2)
])
assert not df_equal(exp1, exp2)
exp1 = pandas.DataFrame([
dict(k1="a", k2="b", v=numpy.nan, i=1),
dict(k1="b", k2="b", v=1, i=2)
])
exp2 = pandas.DataFrame([
dict(k1="a", k2="b", v=numpy.nan, i=1),
dict(k1="b", k2="b", v=1, i=2)
])
assert not df_equal(exp1, exp2)
def test_groupby_sort_head(self):
fLOG(
__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
l = [dict(k1="a", k2="b", v=4, i=1),
dict(k1="a", k2="b", v=5, i=1),
dict(k1="a", k2="b", v=4, i=2),
dict(k1="b", k2="b", v=1, i=2),
dict(k1="b", k2="b", v=1, i=3),
]
exp = [dict(k1="a", k2="b", v=4, i=1),
dict(k1="b", k2="b", v=1, i=2),
]
df = pandas.DataFrame(l)
exp = pandas.DataFrame(exp)
res = groupby_topn(df, by_keys=["k1", "k2"],
sort_keys=["v", "i"], as_index=False)
b = df_equal(exp, res)
if not b:
raise Exception(
"dataframe not equal\nRES:\n{0}\nEXP\n{1}".format(str(res), str(exp)))
def test_groupby_sort_head(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
ld = [
dict(k1="a", k2="b", v=4, i=1),
dict(k1="a", k2="b", v=5, i=1),
dict(k1="a", k2="b", v=4, i=2),
dict(k1="b", k2="b", v=1, i=2),
dict(k1="b", k2="b", v=1, i=3)
]
exp = [dict(k1="a", k2="b", v=4, i=1), dict(k1="b", k2="b", v=1, i=2)]
df = pandas.DataFrame(ld)
exp = pandas.DataFrame(exp)
res = groupby_topn(df,
by_keys=["k1", "k2"],
sort_keys=["v", "i"],
as_index=False)
b = df_equal(exp, res)
if not b:
raise Exception("dataframe not equal\nRES:\n{0}\nEXP\n{1}".format(
str(res), str(exp)))
def test_faq_pandas(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
df = pandas.DataFrame([{"a": 1}])
if False:
# does not work
df_to_clipboard(df)
assert df_equal(df, df)
speed_dataframe()
def test_faq_pandas(self):
fLOG(
__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
df = pandas.DataFrame([{"a": 1}])
if False:
# does not work
df_to_clipboard(df)
assert df_equal(df, df)
speed_dataframe()
def test_df_equal(self):
fLOG(
__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
exp1 = pandas.DataFrame(
[dict(k1="a", k2="b", v=4, i=1), dict(k1="b", k2="b", v=1, i=2)])
exp2 = pandas.DataFrame(
[dict(k1="a", k2="b", v=4, i=1), dict(k1="b", k2="b", v=1, i=2)])
assert df_equal(exp1, exp2)
exp1 = pandas.DataFrame(
[dict(k1="a", k2="b", v=4, i=1), dict(k1="b", k2="b", v=1, i=2)])
exp2 = pandas.DataFrame(
[dict(k1="a", k2="b", v=4, i=1), dict(k1="b", k2="b", v=1, i=2)])
exp2 = exp2[["k2", "k1", "v", "i"]]
assert df_equal(exp1, exp2)
exp1 = pandas.DataFrame(
[dict(k1="a", k2="b", v=4, i=1), dict(k1="b", k2="b", v=1, i=2)])
exp2 = pandas.DataFrame(
[dict(k1="a", k2="b", v=3, i=1), dict(k1="b", k2="b", v=1, i=2)])
assert not df_equal(exp1, exp2)
exp1 = pandas.DataFrame(
[dict(k1="a", k2="b", v=4, i=1), dict(k1="b", k2="b", v=1, i=2)])
exp2 = pandas.DataFrame(
[dict(k1="a", k2="b", v=numpy.nan, i=1), dict(k1="b", k2="b", v=1, i=2)])
assert not df_equal(exp1, exp2)
exp1 = pandas.DataFrame(
[dict(k1="a", k2="b", v=numpy.nan, i=1), dict(k1="b", k2="b", v=1, i=2)])
exp2 = pandas.DataFrame(
[dict(k1="a", k2="b", v=numpy.nan, i=1), dict(k1="b", k2="b", v=1, i=2)])
assert not df_equal(exp1, exp2)
