def test_n(self):
df = data.df_diamonds >> gr.tf_select(X.cut, X.x) >> gr.tf_head(5)
# straight summarize
t = df >> gr.tf_summarize(n=gr.n(X.x))
df_truth = pd.DataFrame({"n": [5]})
self.assertTrue(t.equals(df_truth))
# grouped summarize
t = df >> gr.tf_group_by(X.cut) >> gr.tf_summarize(n=gr.n(X.x))
df_truth = pd.DataFrame({
"cut": ["Good", "Ideal", "Premium"],
"n": [2, 1, 2]
})
self.assertTrue(t.equals(df_truth))
# straight mutate
t = df >> gr.tf_mutate(n=gr.n(X.x))
df_truth = df.copy()
df_truth["n"] = 5
self.assertTrue(t.equals(df_truth))
# grouped mutate
t = df >> gr.tf_group_by(X.cut) >> gr.tf_mutate(n=gr.n(X.x))
df_truth["n"] = pd.Series([1, 2, 2, 2, 2, 2])
self.assertTrue(t.sort_index().equals(df_truth))
# Implicit mode summarize
t = df >> gr.tf_summarize(n=gr.n())
df_truth = pd.DataFrame({"n": [5]})
self.assertTrue(t.equals(df_truth))
# Implicit mode mutate
t = df >> gr.tf_group_by(X.cut) >> gr.tf_mutate(n=gr.n())
df_truth = df.copy()
df_truth["n"] = pd.Series([1, 2, 2, 2, 2, 2])
self.assertTrue(t.sort_index().equals(df_truth))
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_last(self):
df = data.df_diamonds >> gr.tf_select(X.cut, X.x) >> gr.tf_head(5)
# straight summarize
t = df >> gr.tf_summarize(l=gr.last(X.x))
df_truth = pd.DataFrame({"l": [4.34]})
self.assertTrue(t.equals(df_truth))
# grouped summarize
t = df >> gr.tf_group_by(X.cut) >> gr.tf_summarize(l=gr.last(X.x))
df_truth = pd.DataFrame({
"cut": ["Good", "Ideal", "Premium"],
"l": [4.34, 3.95, 4.20]
})
self.assertTrue(t.equals(df_truth))
# summarize with order_by
t = df >> gr.tf_summarize(f=gr.last(
X.x, order_by=[gr.desc(X.cut), gr.desc(X.x)]))
df_truth = pd.DataFrame({"f": [4.05]})
assert df_truth.equals(t)
# straight mutate
t = df >> gr.tf_mutate(l=gr.last(X.x))
df_truth = df.copy()
df_truth["l"] = df_truth.x.iloc[4]
self.assertTrue(t.equals(df_truth))
# grouped mutate
t = df >> gr.tf_group_by(X.cut) >> gr.tf_mutate(l=gr.last(X.x))
df_truth["l"] = pd.Series([3.95, 4.20, 4.34, 4.20, 4.34])
self.assertTrue(t.sort_index().equals(df_truth))
def test_nth(self):
df = data.df_diamonds >> gr.tf_select(X.cut, X.x) >> gr.tf_head(10)
# straight summarize
t = df >> gr.tf_summarize(second=gr.nth(X.x, 1))
df_truth = pd.DataFrame({"second": [3.89]})
self.assertTrue(t.equals(df_truth))
# grouped summarize
t = df >> gr.tf_group_by(
X.cut) >> gr.tf_summarize(first=gr.nth(X.x, 0))
df_truth = pd.DataFrame({
"cut": ["Fair", "Good", "Ideal", "Premium", "Very Good"],
"first": [3.87, 4.05, 3.95, 3.89, 3.94],
})
self.assertTrue(t.equals(df_truth))
# summarize with order_by
t = df >> gr.tf_summarize(last=gr.nth(
X.x, -1, order_by=[gr.desc(X.cut), gr.desc(X.x)]))
df_truth = pd.DataFrame({"last": [3.87]})
self.assertTrue(df_truth.equals(t))
# straight mutate
t = df >> gr.tf_mutate(out_of_range=gr.nth(X.x, 500))
df_truth = df.copy()
df_truth["out_of_range"] = np.nan
self.assertTrue(t.equals(df_truth))
# grouped mutate
t = df >> gr.tf_group_by(
X.cut) >> gr.tf_mutate(penultimate=gr.nth(X.x, -2))
df_truth = df.copy()
df_truth["penultimate"] = pd.Series(
[np.nan, 3.89, 4.05, 3.89, 4.05, 4.07, 4.07, 4.07, np.nan, 4.07])
self.assertTrue(t.sort_index().equals(df_truth))
def test_first(self):
df = data.df_diamonds >> gr.tf_select(X.cut, X.x) >> gr.tf_head(5)
# straight summarize
t = df >> gr.tf_summarize(f=gr.first(X.x))
df_truth = pd.DataFrame({"f": [3.95]})
self.assertTrue(t.equals(df_truth))
# grouped summarize
t = df >> gr.tf_group_by(X.cut) >> gr.tf_summarize(f=gr.first(X.x))
df_truth = pd.DataFrame({
"cut": ["Good", "Ideal", "Premium"],
"f": [4.05, 3.95, 3.89]
})
self.assertTrue(t.equals(df_truth))
# summarize with order_by
t = df >> gr.tf_summarize(f=gr.first(X.x, order_by=gr.desc(X.cut)))
df_truth = pd.DataFrame({"f": [3.89]})
# straight mutate
t = df >> gr.tf_mutate(f=gr.first(X.x))
df_truth = df.copy()
df_truth["f"] = df_truth.x.iloc[0]
self.assertTrue(t.equals(df_truth))
# grouped mutate
t = df >> gr.tf_group_by(X.cut) >> gr.tf_mutate(f=gr.first(X.x))
df_truth["f"] = pd.Series([3.95, 3.89, 4.05, 3.89, 4.05])
self.assertTrue(t.sort_index().equals(df_truth))
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_summarize_each(self):
to_match = pd.DataFrame({
"price_mean": [np.mean(data.df_diamonds.price)],
"price_var": [np.var(data.df_diamonds.price)],
"depth_mean": [np.mean(data.df_diamonds.depth)],
"depth_var": [np.var(data.df_diamonds.depth)],
})
to_match = to_match[[
"price_mean", "price_var", "depth_mean", "depth_var"
]]
test1 = data.df_diamonds >> gr.tf_summarize_each([np.mean, np.var],
X.price, 4)
test2 = data.df_diamonds >> gr.tf_summarize_each([np.mean, np.var],
X.price, "depth")
self.assertTrue(to_match.equals(test1))
self.assertTrue(to_match.equals(test2))
group = pd.DataFrame(
{"cut": ["Fair", "Good", "Ideal", "Premium", "Very Good"]})
group["price_mean"] = [
np.mean(data.df_diamonds[data.df_diamonds.cut == c].price)
for c in group.cut.values
]
group["price_var"] = [
np.var(data.df_diamonds[data.df_diamonds.cut == c].price)
for c in group.cut.values
]
group["depth_mean"] = [
np.mean(data.df_diamonds[data.df_diamonds.cut == c].depth)
for c in group.cut.values
]
group["depth_var"] = [
np.var(data.df_diamonds[data.df_diamonds.cut == c].depth)
for c in group.cut.values
]
group = group[[
"cut", "price_mean", "price_var", "depth_mean", "depth_var"
]]
test1 = (data.df_diamonds >> gr.tf_group_by(X.cut) >>
gr.tf_summarize_each([np.mean, np.var], X.price, 4))
test2 = (data.df_diamonds >> gr.tf_group_by("cut") >>
gr.tf_summarize_each([np.mean, np.var], X.price, "depth"))
self.assertTrue(group.equals(test1))
self.assertTrue(group.equals(test2))
def test_mean_ci(self):
# Basic functionality
y = pd.Series([-1, -1, 0, +1, +1]) # sd == 1
lo_true = 0 - (-norm.ppf(0.005)) * 1 / np.sqrt(5)
up_true = 0 + (-norm.ppf(0.005)) * 1 / np.sqrt(5)
self.assertTrue((lo_true - gr.mean_lo(y, alpha=0.005)) < 1e-6)
self.assertTrue((up_true - gr.mean_up(y, alpha=0.005)) < 1e-6)
# Grouped functionality
df = (gr.df_grid(
y=[-1, -1, 0, +1, +1],
x=[0, 1],
) >> gr.tf_mutate(y=X.y + X.x) >> gr.tf_group_by(X.x) >>
gr.tf_summarize(
mean_lo=gr.mean_lo(X.y),
mean_up=gr.mean_up(X.y),
))
self.assertTrue((df[df.x == 0].mean_lo.values[0] - lo_true) < 1e-6)
self.assertTrue((df[df.x == 0].mean_up.values[0] - up_true) < 1e-6)
self.assertTrue((df[df.x == 1].mean_lo.values[0] -
(lo_true + 1)) < 1e-6)
self.assertTrue((df[df.x == 1].mean_up.values[0] -
(up_true + 1)) < 1e-6)
def test_group_transmute(self):
df = data.df_diamonds.copy()
df = df.groupby("cut").apply(group_mutate_helper).reset_index(
drop=True)
df = df[["cut", "testcol"]]
d = (data.df_diamonds >> gr.tf_group_by("cut") >>
gr.tf_transmute(testcol=X.x * X.shape[0]))
self.assertTrue(df.equals(d.sort_index()))
def test_max(self):
df = data.df_diamonds >> gr.tf_select(X.cut, X.x) >> gr.tf_head(5)
# straight summarize
t = df >> gr.tf_summarize(m=gr.max(X.x))
df_truth = pd.DataFrame({"m": [4.34]})
self.assertTrue(t.equals(df_truth))
# grouped summarize
t = df >> gr.tf_group_by(X.cut) >> gr.tf_summarize(m=gr.max(X.x))
df_truth = pd.DataFrame(
{"cut": ["Good", "Ideal", "Premium"], "m": [4.34, 3.95, 4.20]}
)
self.assertTrue(t.equals(df_truth))
# straight mutate
t = df >> gr.tf_mutate(m=gr.max(X.x))
df_truth = df.copy()
df_truth["m"] = 4.34
self.assertTrue(t.equals(df_truth))
# grouped mutate
t = df >> gr.tf_group_by(X.cut) >> gr.tf_mutate(m=gr.max(X.x))
df_truth["m"] = pd.Series([3.95, 4.20, 4.34, 4.20, 4.34])
self.assertTrue(t.sort_index().equals(df_truth))
def test_IQR(self):
df = data.df_diamonds >> gr.tf_select(X.cut, X.x) >> gr.tf_head(5)
# straight summarize
t = df >> gr.tf_summarize(i=gr.IQR(X.x))
df_truth = pd.DataFrame({"i": [0.25]})
self.assertTrue(t.equals(df_truth))
# grouped summarize
t = df >> gr.tf_group_by(X.cut) >> gr.tf_summarize(i=gr.IQR(X.x))
df_truth = pd.DataFrame(
{"cut": ["Good", "Ideal", "Premium"], "i": [0.145, 0.000, 0.155]}
)
test_vector = abs(t.i - df_truth.i)
assert all(test_vector < 0.000000001)
# straight mutate
t = df >> gr.tf_mutate(i=gr.IQR(X.x))
df_truth = df.copy()
df_truth["i"] = 0.25
self.assertTrue(t.equals(df_truth))
# grouped mutate
t = df >> gr.tf_group_by(X.cut) >> gr.tf_mutate(i=gr.IQR(X.x))
df_truth["i"] = pd.Series([0.000, 0.155, 0.145, 0.155, 0.145])
test_vector = abs(t.i - df_truth.i)
self.assertTrue(all(test_vector < 0.000000001))
def test_n_distinct(self):
df = pd.DataFrame({
"col_1": ["a", "a", "a", "b", "b", "b", "c", "c"],
"col_2": [1, 1, 1, 2, 3, 3, 4, 5],
})
# straight summarize
t = df >> gr.tf_summarize(n=gr.n_distinct(X.col_2))
df_truth = pd.DataFrame({"n": [5]})
self.assertTrue(t.equals(df_truth))
# grouped summarize
t = df >> gr.tf_group_by(
X.col_1) >> gr.tf_summarize(n=gr.n_distinct(X.col_2))
df_truth = pd.DataFrame({"col_1": ["a", "b", "c"], "n": [1, 2, 2]})
self.assertTrue(t.equals(df_truth))
# straight mutate
t = df >> gr.tf_mutate(n=gr.n_distinct(X.col_2))
df_truth = df.copy()
df_truth["n"] = 5
self.assertTrue(t.equals(df_truth))
# grouped mutate
t = df >> gr.tf_group_by(
X.col_1) >> gr.tf_mutate(n=gr.n_distinct(X.col_2))
df_truth["n"] = pd.Series([1, 1, 1, 2, 2, 2, 2, 2])
self.assertTrue(t.equals(df_truth))
def test_summarize(self):
p = pd.DataFrame({
"price_mean": [data.df_diamonds.price.mean()],
"price_std": [data.df_diamonds.price.std()],
})
self.assertTrue(
p.equals(data.df_diamonds >> gr.tf_summarize(
price_mean=X.price.mean(), price_std=X.price.std())))
pcut = pd.DataFrame(
{"cut": ["Fair", "Good", "Ideal", "Premium", "Very Good"]})
pcut["price_mean"] = [
data.df_diamonds[data.df_diamonds.cut == c].price.mean()
for c in pcut.cut.values
]
pcut["price_std"] = [
data.df_diamonds[data.df_diamonds.cut == c].price.std()
for c in pcut.cut.values
]
self.assertTrue(
pcut.equals(
data.df_diamonds >> gr.tf_group_by("cut") >> gr.tf_summarize(
price_mean=X.price.mean(), price_std=X.price.std())))
def test_group_attributes(self):
d = data.df_diamonds >> gr.tf_group_by("cut")
self.assertTrue(hasattr(d, "_grouped_by"))
self.assertTrue(d._grouped_by == [
"cut",
])
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()))
