def test_calc_cost_to_own_ioniq(df_filtered_cars):
auto = "hyundai ioniq hybrid plugin"
df = _get_df_with_cost_to_own(auto, filtered_cars=df_filtered_cars)
assert len(df) > 1
for index, car in df.iterrows():
assert car[Column.MY_M_COSTS] > 400
assert car[Column.MY_M_COSTS] < 600
def test_euro_columns(df_filtered_cars):
df = _get_df_with_cost_to_own(filtered_cars=df_filtered_cars)
for col in df.columns:
for i, row in df.iterrows():
val = row[col]
if type(val) == str:
assert "euro" not in row[col].lower(), f"Found euro in '{col}'"
def test_calc_cost_to_own_corsa(df_filtered_cars):
auto = "opel corsa"
df = _get_df_with_cost_to_own(auto, filtered_cars=df_filtered_cars)
assert len(df) > 1
for index, car in df.iterrows():
assert 300 < car[Column.MY_M_COSTS] < 500
assert car[Column.ACCELERATION] < 11
def test_calc_cost_to_own_ceed(df_filtered_cars):
auto = "kia ceed"
df = _get_df_with_cost_to_own(auto, filtered_cars=df_filtered_cars)
assert len(df) > 1
for index, car in df.iterrows():
assert car[Column.MY_M_COSTS] > 300
assert car[Column.MY_M_COSTS] < 550
assert car[Column.ACCELERATION] < 12
def test_calc_cost_to_own_corsa_e_with_german_discount(df_filtered_cars):
auto = "opel corsa-e"
df = _get_df_with_cost_to_own(auto,
de_discount=True,
filtered_cars=df_filtered_cars)
assert len(df) > 1
for index, car in df.iterrows():
is_electric = car["Motorart"] == "Elektro"
if is_electric:
assert car[Column.TOTAL_PRICE] < car[Column.PRICE]
else:
assert car[Column.TOTAL_PRICE] >= car[Column.PRICE]
def test_calc_cost_to_own_ioniq_with_german_discount(df_filtered_cars):
auto = "hyundai ioniq"
df = _get_df_with_cost_to_own(auto,
de_discount=True,
filtered_cars=df_filtered_cars)
assert len(df) > 1
for index, car in df.iterrows():
is_electric = car["Motorart"] in ["PlugIn-Hybrid", "Elektro"]
if is_electric:
assert car[Column.TOTAL_PRICE] < car[Column.PRICE]
else:
assert (pd.isna(car[Column.TOTAL_PRICE])
or car[Column.TOTAL_PRICE] >= car[Column.PRICE])
def test_calc_cost_to_own_208(df_filtered_cars):
auto = "Peugeot 208"
df = _get_df_with_cost_to_own(auto, filtered_cars=df_filtered_cars)
assert len(df) > 1
for _, car in df.iterrows():
assert 300 < car[Column.MY_M_COSTS] < 510
capacity = car[Column.BATTERY_CAPACITY]
tank = car[Column.FUEL_TANK_SIZE]
if car[Column.ENGINE_TYPE] == "Elektro":
assert pd.notna(capacity)
assert pd.isna(tank)
else:
assert pd.notna(tank)
assert pd.isna(capacity)
def test_calc_cost_to_own(df_filtered_cars):
auto = "subaru impreza"
df = _get_df_with_cost_to_own(auto, filtered_cars=df_filtered_cars)
assert len(df) > 1
for index, car in df.iterrows():
assert car[Column.MY_M_COSTS] > 500
assert car[Column.MY_M_COSTS] < 700
assert car[Column.ACCELERATION] < 13
assert car[Column.ACCELERATION] > 8
assert car[Column.RANGE] > 500
assert car[Column.TOTAL_PRICE] > 0
assert not df[df[Column.NAME].str.contains(
"Subaru Impreza 2.0i Exclusive")].empty
def test_missing_features_in_adac(df_filtered_cars):
df_input = pd.read_excel(
os.path.join(os.path.dirname(__file__), "cars.xlsx"))
cols = set(["name"] + df_input["adac column"].dropna().to_list()) - set(
"LED-Scheinwerfer,Navigation,Querverkehrassistent,Aktive Kopfstützen,Fußgängererkennung,Kurvenlicht,Notbremsassistent,Spurhalteassistent,Stauassistent,Verkehrsschild-Erkennung"
.split(","))
df = _get_df_with_cost_to_own(filtered_cars=df_filtered_cars)
df_bad = pd.DataFrame()
for i, row in df[cols].iterrows():
nas = row[pd.isna(row[cols])]
s_row = row.filter(["name"]).append(nas)
if nas.size > 0:
df_bad = df_bad.append(s_row, ignore_index=True)
df_bad.to_excel("/tmp/bad.xlsx")
assert df_bad.shape[0] / float(df.shape[0]) < 0.10
def test_calc_cost_to_own_3008(df_filtered_cars):
auto = "peugeot 3008"
df = _get_df_with_cost_to_own(auto, filtered_cars=df_filtered_cars)
assert len(df) >= 2
def test_calc_cost_to_own_bmw_1(df_filtered_cars):
auto = "Bmw 1"
df = _get_df_with_cost_to_own(auto, filtered_cars=df_filtered_cars)
assert len(df) > 1
def test_calc_cost_to_own_mb_a(df_filtered_cars):
auto = "Mercedes A"
df = _get_df_with_cost_to_own(auto, filtered_cars=df_filtered_cars)
assert not df[df[Column.NAME].str.contains("Mercedes A")].empty