def test_arrange(self):
df = (
data.df_diamonds.groupby("cut")
.apply(arrange_apply_helperfunc)
.reset_index(drop=True)
)
d = (
data.df_diamonds
>> gr.tf_group_by("cut")
>> gr.tf_arrange("depth", ascending=False)
>> gr.tf_head(5)
>> gr.tf_ungroup()
).reset_index(drop=True)
self.assertTrue(df.equals(d))
d = (
data.df_diamonds
>> gr.tf_group_by("cut")
>> gr.tf_arrange(X.depth, ascending=False)
>> gr.tf_head(5)
>> gr.tf_ungroup()
).reset_index(drop=True)
assert df.equals(d)
df = data.df_diamonds.sort_values(["cut", "price"], ascending=False)
d = data.df_diamonds >> gr.tf_arrange(gr.desc(X.cut), gr.desc(X.price))
self.assertTrue(df.equals(d))
def test_sd(self):
df = (
data.df_diamonds
>> gr.tf_group_by(X.cut)
>> gr.tf_head(3)
>> gr.tf_select(X.cut, X.x)
>> gr.tf_ungroup()
)
# straight summarize
t = df >> gr.tf_summarize(s=gr.sd(X.x))
df_truth = pd.DataFrame({"s": [0.829091]})
test_vector = abs(t.s - df_truth.s)
self.assertTrue(all(test_vector < 0.00001))
# grouped summarize
t = df >> gr.tf_group_by(X.cut) >> gr.tf_summarize(s=gr.sd(X.x))
df_truth = pd.DataFrame(
{
"cut": ["Fair", "Good", "Ideal", "Premium", "Very Good"],
"s": [1.440417, 0.148436, 0.236925, 0.181934, 0.072342],
}
)
test_vector = abs(t.s - df_truth.s)
self.assertTrue(all(test_vector < 0.00001))
# straight mutate
t = df >> gr.tf_mutate(s=gr.sd(X.x))
df_truth = df.copy()
df_truth["s"] = 0.829091
test_vector = abs(t.s - df_truth.s)
self.assertTrue(all(test_vector < 0.00001))
# grouped mutate
t = df >> gr.tf_group_by(X.cut) >> gr.tf_mutate(s=gr.sd(X.x))
# df_truth['s'] = pd.Series([1.440417, 1.440417, 1.440417, 0.148436, 0.148436, 0.148436,
# 0.236925, 0.236925, 0.236925, 0.181934, 0.181934, 0.181934,
# 0.072342, 0.072342, 0.072342],
# index=t.index)
# test_vector = abs(t.s - df_truth.s)
# print(t)
# print(df_truth)
self.assertTrue(all(test_vector < 0.00001))
# test with single value (var undefined)
df = (
data.df_diamonds
>> gr.tf_group_by(X.cut)
>> gr.tf_head(1)
>> gr.tf_select(X.cut, X.x)
)
t = df >> gr.tf_group_by(X.cut) >> gr.tf_summarize(s=gr.sd(X.x))
df_truth = pd.DataFrame(
{
"cut": ["Fair", "Good", "Ideal", "Premium", "Very Good"],
"s": [np.nan, np.nan, np.nan, np.nan, np.nan],
}
)
self.assertTrue(t.equals(df_truth))
def test_var(self):
df = (
data.df_diamonds
>> gr.tf_group_by(X.cut)
>> gr.tf_head(3)
>> gr.tf_select(X.cut, X.x)
>> gr.tf_ungroup()
)
# straight summarize
t = df >> gr.tf_summarize(v=gr.var(X.x))
df_truth = pd.DataFrame({"v": [0.687392]})
test_vector = abs(t.v - df_truth.v)
self.assertTrue(all(test_vector < 0.00001))
# grouped summarize
t = df >> gr.tf_group_by(X.cut) >> gr.tf_summarize(v=gr.var(X.x))
df_truth = pd.DataFrame(
{
"cut": ["Fair", "Good", "Ideal", "Premium", "Very Good"],
"v": [2.074800, 0.022033, 0.056133, 0.033100, 0.005233],
}
)
test_vector = abs(t.v - df_truth.v)
self.assertTrue(all(test_vector < 0.00001))
# straight mutate
t = df >> gr.tf_mutate(v=gr.var(X.x))
df_truth = df.copy()
df_truth["v"] = 0.687392
test_vector = abs(t.v - df_truth.v)
self.assertTrue(all(test_vector < 0.00001))
# grouped mutate
# t = df >> group_by(X.cut) >> mutate(v=var(X.x))
# df_truth['v'] = pd.Series([2.074800, 2.074800, 2.074800, 0.022033, 0.022033, 0.022033,
# 0.056133, 0.056133, 0.056133, 0.033100, 0.033100, 0.033100,
# 0.005233, 0.005233, 0.005233],
# index=t.index)
# test_vector = abs(t.v - df_truth.v)
# assert all(test_vector < .00001)
# test with single value (var undefined)
df = (
data.df_diamonds
>> gr.tf_group_by(X.cut)
>> gr.tf_head(1)
>> gr.tf_select(X.cut, X.x)
)
t = df >> gr.tf_group_by(X.cut) >> gr.tf_summarize(v=gr.var(X.x))
df_truth = pd.DataFrame(
{
"cut": ["Fair", "Good", "Ideal", "Premium", "Very Good"],
"v": [np.nan, np.nan, np.nan, np.nan, np.nan],
}
)
self.assertTrue(t.equals(df_truth))
def test_median(self):
df = (
data.df_diamonds
>> gr.tf_group_by(X.cut)
>> gr.tf_head(3)
>> gr.tf_select(X.cut, X.x)
>> gr.tf_ungroup()
)
# straight summarize
t = df >> gr.tf_summarize(m=gr.median(X.x))
df_truth = pd.DataFrame({"m": [4.05]})
self.assertTrue(t.equals(df_truth))
# grouped summarize
t = df >> gr.tf_group_by(X.cut) >> gr.tf_summarize(m=gr.median(X.x))
df_truth = pd.DataFrame(
{
"cut": ["Fair", "Good", "Ideal", "Premium", "Very Good"],
"m": [6.27, 4.25, 3.95, 3.89, 3.95],
}
)
self.assertTrue(t.equals(df_truth))
# straight mutate
t = df >> gr.tf_mutate(m=gr.median(X.x))
df_truth = df.copy()
df_truth["m"] = 4.05
self.assertTrue(t.equals(df_truth))
# grouped mutate
# t = df >> group_by(X.cut) >> mutate(m=median(X.x))
# df_truth['m'] = pd.Series(
# [6.27, 6.27, 6.27, 4.25, 4.25, 4.25, 3.95, 3.95, 3.95, 3.89, 3.89, 3.89, 3.95, 3.95, 3.95],
# index=t.index)
# assert t.equals(df_truth)
# make sure it handles case with even counts properly
df = (
data.df_diamonds
>> gr.tf_group_by(X.cut)
>> gr.tf_head(2)
>> gr.tf_select(X.cut, X.x)
)
t = df >> gr.tf_group_by(X.cut) >> gr.tf_summarize(m=gr.median(X.x))
df_truth = pd.DataFrame(
{
"cut": ["Fair", "Good", "Ideal", "Premium", "Very Good"],
"m": [5.160, 4.195, 3.940, 4.045, 3.945],
}
)
test_vector = abs(t.m - df_truth.m)
self.assertTrue(all(test_vector < 0.000000001))
def test_group_mutate(self):
df = data.df_diamonds.copy()
df = df.groupby("cut").apply(group_mutate_helper)
d = (data.df_diamonds >> gr.tf_group_by("cut") >>
gr.tf_mutate(testcol=X.x * X.shape[0]) >> gr.tf_ungroup())
self.assertTrue(df.equals(d.sort_index()))