def test_compare_should_not_side_effect():
_timeseries_df2 = timeseries_df2.copy()
pp.compare(
df=_timeseries_df2,
source_columns=["y"],
compare_columns=["z"],
compare_type=PPC.DIFF,
)
assert _timeseries_df2.equals(timeseries_df2)
def test_compare(self):
# `difference` comparison
post_df = proc.compare(
df=timeseries_df2,
source_columns=["y"],
compare_columns=["z"],
compare_type="difference",
)
self.assertListEqual(
post_df.columns.tolist(), ["label", "y", "z", "difference__y__z",]
)
self.assertListEqual(
series_to_list(post_df["difference__y__z"]), [0.0, -2.0, -8.0, -6.0],
)
# drop original columns
post_df = proc.compare(
df=timeseries_df2,
source_columns=["y"],
compare_columns=["z"],
compare_type="difference",
drop_original_columns=True,
)
self.assertListEqual(post_df.columns.tolist(), ["label", "difference__y__z",])
# `percentage` comparison
post_df = proc.compare(
df=timeseries_df2,
source_columns=["y"],
compare_columns=["z"],
compare_type="percentage",
)
self.assertListEqual(
post_df.columns.tolist(), ["label", "y", "z", "percentage__y__z",]
)
self.assertListEqual(
series_to_list(post_df["percentage__y__z"]), [0.0, -0.5, -0.8, -0.75],
)
# `ratio` comparison
post_df = proc.compare(
df=timeseries_df2,
source_columns=["y"],
compare_columns=["z"],
compare_type="ratio",
)
self.assertListEqual(
post_df.columns.tolist(), ["label", "y", "z", "ratio__y__z",]
)
self.assertListEqual(
series_to_list(post_df["ratio__y__z"]), [1.0, 0.5, 0.2, 0.25],
)
def test_compare_percentage():
# `percentage` comparison
post_df = pp.compare(
df=timeseries_df2,
source_columns=["y"],
compare_columns=["z"],
compare_type=PPC.PCT,
)
"""
label y z percentage__y__z
2019-01-01 x 2.0 2.0 0.0
2019-01-02 y 2.0 4.0 -0.50
2019-01-05 z 2.0 10.0 -0.80
2019-01-07 q 2.0 8.0 -0.75
"""
assert post_df.equals(
pd.DataFrame(
index=timeseries_df2.index,
data={
"label": ["x", "y", "z", "q"],
"y": [2.0, 2.0, 2.0, 2.0],
"z": [2.0, 4.0, 10.0, 8.0],
"percentage__y__z": [0.0, -0.50, -0.80, -0.75],
},
))
def test_compare_ratio():
# `ratio` comparison
post_df = pp.compare(
df=timeseries_df2,
source_columns=["y"],
compare_columns=["z"],
compare_type=PPC.RAT,
)
"""
label y z ratio__y__z
2019-01-01 x 2.0 2.0 1.00
2019-01-02 y 2.0 4.0 0.50
2019-01-05 z 2.0 10.0 0.20
2019-01-07 q 2.0 8.0 0.25
"""
assert post_df.equals(
pd.DataFrame(
index=timeseries_df2.index,
data={
"label": ["x", "y", "z", "q"],
"y": [2.0, 2.0, 2.0, 2.0],
"z": [2.0, 4.0, 10.0, 8.0],
"ratio__y__z": [1.00, 0.50, 0.20, 0.25],
},
))
def test_compare_after_pivot():
pivot_df = pp.pivot(
df=multiple_metrics_df,
index=["dttm"],
columns=["country"],
aggregates={
"sum_metric": {
"operator": "sum"
},
"count_metric": {
"operator": "sum"
},
},
flatten_columns=False,
reset_index=False,
)
"""
count_metric sum_metric
country UK US UK US
dttm
2019-01-01 1 2 5 6
2019-01-02 3 4 7 8
"""
compared_df = pp.compare(
pivot_df,
source_columns=["count_metric"],
compare_columns=["sum_metric"],
compare_type=PPC.DIFF,
drop_original_columns=True,
)
"""
difference__count_metric__sum_metric
country UK US
dttm
2019-01-01 -4 -4
2019-01-02 -4 -4
"""
flat_df = pp.flatten(compared_df)
"""
dttm difference__count_metric__sum_metric, UK difference__count_metric__sum_metric, US
0 2019-01-01 -4 -4
1 2019-01-02 -4 -4
"""
assert flat_df.equals(
pd.DataFrame(
data={
"dttm":
pd.to_datetime(["2019-01-01", "2019-01-02"]),
FLAT_COLUMN_SEPARATOR.join([
"difference__count_metric__sum_metric", "UK"
]): [-4, -4],
FLAT_COLUMN_SEPARATOR.join([
"difference__count_metric__sum_metric", "US"
]): [-4, -4],
}))
def test_compare():
# `difference` comparison
post_df = compare(
df=timeseries_df2,
source_columns=["y"],
compare_columns=["z"],
compare_type="difference",
)
assert post_df.columns.tolist() == ["label", "y", "z", "difference__y__z"]
assert series_to_list(
post_df["difference__y__z"]) == [0.0, -2.0, -8.0, -6.0]
# drop original columns
post_df = compare(
df=timeseries_df2,
source_columns=["y"],
compare_columns=["z"],
compare_type="difference",
drop_original_columns=True,
)
assert post_df.columns.tolist() == ["label", "difference__y__z"]
# `percentage` comparison
post_df = compare(
df=timeseries_df2,
source_columns=["y"],
compare_columns=["z"],
compare_type="percentage",
)
assert post_df.columns.tolist() == ["label", "y", "z", "percentage__y__z"]
assert series_to_list(
post_df["percentage__y__z"]) == [0.0, -0.5, -0.8, -0.75]
# `ratio` comparison
post_df = compare(
df=timeseries_df2,
source_columns=["y"],
compare_columns=["z"],
compare_type="ratio",
)
assert post_df.columns.tolist() == ["label", "y", "z", "ratio__y__z"]
assert series_to_list(post_df["ratio__y__z"]) == [1.0, 0.5, 0.2, 0.25]
def test_compare_diff():
# `difference` comparison
post_df = pp.compare(
df=timeseries_df2,
source_columns=["y"],
compare_columns=["z"],
compare_type=PPC.DIFF,
)
"""
label y z difference__y__z
2019-01-01 x 2.0 2.0 0.0
2019-01-02 y 2.0 4.0 -2.0
2019-01-05 z 2.0 10.0 -8.0
2019-01-07 q 2.0 8.0 -6.0
"""
assert post_df.equals(
pd.DataFrame(
index=timeseries_df2.index,
data={
"label": ["x", "y", "z", "q"],
"y": [2.0, 2.0, 2.0, 2.0],
"z": [2.0, 4.0, 10.0, 8.0],
"difference__y__z": [0.0, -2.0, -8.0, -6.0],
},
))
# drop original columns
post_df = pp.compare(
df=timeseries_df2,
source_columns=["y"],
compare_columns=["z"],
compare_type=PPC.DIFF,
drop_original_columns=True,
)
assert post_df.equals(
pd.DataFrame(
index=timeseries_df2.index,
data={
"label": ["x", "y", "z", "q"],
"difference__y__z": [0.0, -2.0, -8.0, -6.0],
},
))
def test_compare_multi_index_column():
index = pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"])
index.name = "__timestamp"
iterables = [["m1", "m2"], ["a", "b"], ["x", "y"]]
columns = pd.MultiIndex.from_product(iterables,
names=[None, "level1", "level2"])
df = pd.DataFrame(index=index, columns=columns, data=1)
"""
m1 m2
level1 a b a b
level2 x y x y x y x y
__timestamp
2021-01-01 1 1 1 1 1 1 1 1
2021-01-02 1 1 1 1 1 1 1 1
2021-01-03 1 1 1 1 1 1 1 1
"""
post_df = pp.compare(
df,
source_columns=["m1"],
compare_columns=["m2"],
compare_type=PPC.DIFF,
drop_original_columns=True,
)
flat_df = pp.flatten(post_df)
"""
__timestamp difference__m1__m2, a, x difference__m1__m2, a, y difference__m1__m2, b, x difference__m1__m2, b, y
0 2021-01-01 0 0 0 0
1 2021-01-02 0 0 0 0
2 2021-01-03 0 0 0 0
"""
assert flat_df.equals(
pd.DataFrame(
data={
"__timestamp":
pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"]),
"difference__m1__m2, a, x": [0, 0, 0],
"difference__m1__m2, a, y": [0, 0, 0],
"difference__m1__m2, b, x": [0, 0, 0],
"difference__m1__m2, b, y": [0, 0, 0],
}))