def test_drop_2_variables_integer_colnames(df_numeric_columns):
transformer = DropFeatures(features_to_drop=[0, 1])
X = transformer.fit_transform(df_numeric_columns)
# expected result
df = pd.DataFrame(
{
2: [20, 21, 19, 18],
3: [0.9, 0.8, 0.7, 0.6],
4: pd.date_range("2020-02-24", periods=4, freq="T"),
}
)
# init params
assert transformer.features_to_drop == [0, 1]
# transform params
pd.testing.assert_frame_equal(X, df)
def test_drop_2_variables(df_vartypes):
transformer = DropFeatures(features_to_drop=["City", "dob"])
X = transformer.fit_transform(df_vartypes)
# expected result
df = pd.DataFrame(
{
"Name": ["tom", "nick", "krish", "jack"],
"Age": [20, 21, 19, 18],
"Marks": [0.9, 0.8, 0.7, 0.6],
}
)
# init params
assert transformer.features_to_drop == ["City", "dob"]
# transform params
assert X.shape == (4, 3)
assert type(X) == pd.DataFrame
pd.testing.assert_frame_equal(X, df)
def test_drop_1_variable(df_vartypes):
transformer = DropFeatures(features_to_drop="City")
X = transformer.fit_transform(df_vartypes)
# expected result
df = pd.DataFrame(
{
"Name": ["tom", "nick", "krish", "jack"],
"Age": [20, 21, 19, 18],
"Marks": [0.9, 0.8, 0.7, 0.6],
"dob": pd.date_range("2020-02-24", periods=4, freq="T"),
}
)
# init params
assert transformer.features_to_drop == "City"
# transform params
assert X.shape == (4, 4)
assert type(X) == pd.DataFrame
pd.testing.assert_frame_equal(X, df)
def test_error_if_empty_list(df_vartypes):
# test case 6: passing an empty list
with pytest.raises(ValueError):
transformer = DropFeatures(features_to_drop=[])
transformer.fit_transform(df_vartypes)
def test_error_when_returning_empty_dataframe(df_vartypes):
# test case 5: dropping all columns produces warning check
with pytest.raises(ValueError):
transformer = DropFeatures(features_to_drop=list(df_vartypes.columns))
transformer.fit_transform(df_vartypes)
def test_error_if_non_existing_variables(df_vartypes):
# test case 2: passing variables that doesn't exist
with pytest.raises(KeyError):
transformer = DropFeatures(features_to_drop=["last_name"])
transformer.fit_transform(df_vartypes)