def test_schema_get_dtype():
"""Test that schema dtype and get_dtype methods handle regex columns."""
schema = DataFrameSchema({
"col1": Column(Int),
"var*": Column(Float, regex=True),
})
data = pd.DataFrame({
"col1": [1, 2, 3],
"var1": [1.0, 1.1, 1.2],
"var2": [1.0, 1.1, 1.2],
"var3": [1.0, 1.1, 1.2],
})
with pytest.warns(UserWarning) as record:
assert schema.dtype == {"col1": Int.str_alias}
assert len(record) == 1
assert record[0].message.args[0].startswith(
"Schema has columns specified as regex column names:")
assert schema.get_dtype(data) == {
"col1": Int.str_alias,
"var1": Float.str_alias,
"var2": Float.str_alias,
"var3": Float.str_alias,
}
def test_check_groupby_multiple_columns():
"""Tests uses of groupby to specify dependencies between one column and a
number of other columns, including error handling."""
schema = DataFrameSchema({
"col1":
Column(
Int,
[
Check(
lambda s: s[("bar", True)].sum() == 16, # 7 + 9
groupby=["col2", "col3"]),
]),
"col2":
Column(String, Check(lambda s: s.isin(["foo", "bar"]))),
"col3":
Column(Bool),
})
df_pass = pd.DataFrame({
"col1": [7, 8, 9, 11, 12, 13],
"col2": ["bar", "bar", "bar", "foo", "foo", "foo"],
"col3": [True, False, True, False, True, False],
})
df = schema.validate(df_pass)
assert isinstance(df, pd.DataFrame)
assert len(df.columns) == 3
assert set(df.columns) == {"col1", "col2", "col3"}
def test_check_function_decorator_errors():
"""Test that the check_input and check_output decorators error properly."""
# case 1: checks that the input and output decorators error when different
# types are passed in and out
@check_input(DataFrameSchema({"column1": Column(Int)}))
@check_output(DataFrameSchema({"column2": Column(Float)}))
def test_func(df):
return df
with pytest.raises(
errors.SchemaError,
match=r"^error in check_input decorator of function"):
test_func(pd.DataFrame({"column2": ["a", "b", "c"]}))
with pytest.raises(
errors.SchemaError,
match=r"^error in check_output decorator of function"):
test_func(pd.DataFrame({"column1": [1, 2, 3]}))
# case 2: check that if the input decorator refers to an index that's not
# in the function signature, it will fail in a way that's easy to interpret
@check_input(DataFrameSchema({"column1": Column(Int)}), 1)
def test_incorrect_check_input_index(df):
return df
with pytest.raises(
errors.SchemaError,
match=r"^error in check_input decorator of function"):
test_incorrect_check_input_index(pd.DataFrame({"column1": [1, 2, 3]})
)
def test_check_function_decorator_transform():
"""Test that transformer argument is in effect in check_input decorator."""
in_schema = DataFrameSchema(
{"column1": Column(Int)},
transformer=lambda df: df.assign(column2="foo"))
out_schema = DataFrameSchema(
{"column1": Column(Int),
"column2": Column(String)})
@check_input(in_schema)
@check_output(out_schema)
def func_input_transform1(df):
return df
result1 = func_input_transform1(pd.DataFrame({"column1": [1, 2, 3]}))
assert "column2" in result1
@check_input(in_schema, 1)
@check_output(out_schema, 1)
def func_input_transform2(_, df):
return _, df
result2 = func_input_transform2(None, pd.DataFrame({"column1": [1, 2, 3]}))
assert "column2" in result2[1]
def test_check_groupby_multiple_columns():
schema = DataFrameSchema({
"col1":
Column(
Int,
[
Check(
lambda s: s[("bar", True)].sum() == 16, # 7 + 9
groupby=["col2", "col3"]),
]),
"col2":
Column(String, Check(lambda s: s.isin(["foo", "bar"]))),
"col3":
Column(Bool),
})
df_pass = pd.DataFrame({
"col1": [7, 8, 9, 11, 12, 13],
"col2": ["bar", "bar", "bar", "foo", "foo", "foo"],
"col3": [True, False, True, False, True, False],
})
df = schema.validate(df_pass)
assert isinstance(df, pd.DataFrame)
assert len(df.columns) == 3
assert set(df.columns) == {"col1", "col2", "col3"}
def test_column_regex_matching(column_name_regex, expected_matches, error):
"""
Column regex pattern matching should yield correct matches and raise
expected errors.
"""
columns = pd.MultiIndex.from_tuples((
("foo_1", "biz_1"),
("foo_2", "baz_1"),
("foo_3", "baz_2"),
("bar_1", "biz_2"),
("bar_2", "biz_3"),
("bar_3", "biz_3"),
))
column_schema = Column(
Int,
Check(lambda s: s >= 0),
name=column_name_regex,
regex=True,
)
if error is not None:
with pytest.raises(error):
column_schema.get_regex_columns(columns)
else:
matched_columns = column_schema.get_regex_columns(columns)
assert expected_matches == matched_columns.tolist()
def test_two_sample_ttest_hypothesis_relationships():
"""Check allowable relationships in two-sample ttest."""
for relationship in Hypothesis.RELATIONSHIPS:
schema = DataFrameSchema({
"height_in_feet": Column(Float, [
Hypothesis.two_sample_ttest(
sample1="M",
sample2="F",
groupby="sex",
relationship=relationship,
alpha=0.5),
]),
"sex": Column(String)
})
assert isinstance(schema, DataFrameSchema)
for relationship in ["foo", "bar", 1, 2, 3, None]:
with pytest.raises(errors.SchemaInitError):
DataFrameSchema({
"height_in_feet": Column(Float, [
Hypothesis.two_sample_ttest(
sample1="M",
sample2="F",
groupby="sex",
relationship=relationship,
alpha=0.5),
]),
"sex": Column(String)
})
def test_column_regex_strict() -> None:
"""Test that Column regex patterns correctly parsed in DataFrameSchema."""
data = pd.DataFrame(
{
"foo_1": [1, 2, 3],
"foo_2": [1, 2, 3],
"foo_3": [1, 2, 3],
}
)
schema = DataFrameSchema(
columns={"foo_*": Column(Int, regex=True)}, strict=True
)
assert isinstance(schema.validate(data), pd.DataFrame)
# adding an extra column in the dataframe should cause error
data = data.assign(bar=[1, 2, 3])
with pytest.raises(errors.SchemaError):
schema.validate(data)
# adding an extra regex column to the schema should pass the strictness
# test
validated_data = schema.add_columns(
{"bar_*": Column(Int, regex=True)}
).validate(data.assign(bar_1=[1, 2, 3]))
assert isinstance(validated_data, pd.DataFrame)
def _validate_score_table(variant_information_table: DataFrame,
score_table: DataFrame):
""" Validate the results of the prioritization method.
The following constraints are checked:
* Each UID from the variant_information_table is also in the score_table
* Each SCORE in the score_table is a numerical value
Parameters
----------
variant_information_table :
The variant information table
score_table :
The scoring results from the prioritization method
Raises
------
:class:`~pandera.errors.SchemaErrors`
If the validation of the data fails
"""
variants_uid = variant_information_table["UID"]
schema = DataFrameSchema({
"UID":
Column(
Int,
Check(lambda x: variants_uid.isin(x) & x.isin(variants_uid)),
required=True),
"SCORE":
Column(Float, coerce=True, required=True)
})
schema.validate(score_table, lazy=True)
def test_one_sample_hypothesis():
"""Check one sample ttest."""
schema = DataFrameSchema({
"height_in_feet": Column(
Float, [
Hypothesis.one_sample_ttest(
popmean=5,
relationship="greater_than",
alpha=0.1),
]
),
})
subset_schema = DataFrameSchema({
"group": Column(String),
"height_in_feet": Column(
Float, [
Hypothesis.one_sample_ttest(
sample="A",
groupby="group",
popmean=5,
relationship="greater_than",
alpha=0.1),
]
),
})
df = (
pd.DataFrame({
"height_in_feet": [8.1, 7, 6.5, 6.7, 5.1],
"group": ["A", "A", "B", "B", "A"],
})
)
schema.validate(df)
subset_schema.validate(df)
def test_column_regex_non_str_types() -> None:
"""Check that column name regex matching excludes non-string types."""
data = pd.DataFrame(
{
1: [1, 2, 3],
2.2: [1, 2, 3],
pd.Timestamp("2018/01/01"): [1, 2, 3],
"foo_1": [1, 2, 3],
"foo_2": [1, 2, 3],
"foo_3": [1, 2, 3],
}
)
schema = DataFrameSchema(
columns={
"foo_": Column(Int, Check.gt(0), regex=True),
r"\d+": Column(Int, Check.gt(0), regex=True),
r"\d+\.\d+": Column(Int, Check.gt(0), regex=True),
"2018-01-01": Column(Int, Check.gt(0), regex=True),
},
)
assert isinstance(schema.validate(data), pd.DataFrame)
# test MultiIndex column case
data = pd.DataFrame(
{
(1, 1): [1, 2, 3],
(2.2, 4.5): [1, 2, 3],
("foo", "bar"): [1, 2, 3],
}
)
schema = DataFrameSchema(
columns={("foo_*", "bar_*"): Column(Int, regex=True)},
)
schema.validate(data)
def test_schema_equality_operators():
"""Test the usage of == for DataFrameSchema, SeriesSchema and
SeriesSchemaBase."""
df_schema = DataFrameSchema({
"col1": Column(Int, Check(lambda s: s >= 0)),
"col2": Column(String, Check(lambda s: s >= 2)),
}, strict=True)
df_schema_columns_in_different_order = DataFrameSchema({
"col2": Column(String, Check(lambda s: s >= 2)),
"col1": Column(Int, Check(lambda s: s >= 0)),
}, strict=True)
series_schema = SeriesSchema(
String,
checks=[Check(lambda s: s.str.startswith("foo"))],
nullable=False,
allow_duplicates=True,
name="my_series")
series_schema_base = SeriesSchemaBase(
String,
checks=[Check(lambda s: s.str.startswith("foo"))],
nullable=False,
allow_duplicates=True,
name="my_series")
not_equal_schema = DataFrameSchema({
"col1": Column(String)
}, strict=False)
assert df_schema == copy.deepcopy(df_schema)
assert df_schema != not_equal_schema
assert df_schema == df_schema_columns_in_different_order
assert series_schema == copy.deepcopy(series_schema)
assert series_schema != not_equal_schema
assert series_schema_base == copy.deepcopy(series_schema_base)
assert series_schema_base != not_equal_schema
def test_multi_index_columns() -> None:
"""Tests that multi-index Columns within DataFrames validate correctly."""
schema = DataFrameSchema(
{
("zero", "foo"): Column(Float, Check(lambda s: (s > 0) & (s < 1))),
("zero", "bar"): Column(
String, Check(lambda s: s.isin(["a", "b", "c", "d"]))
),
("one", "foo"): Column(Int, Check(lambda s: (s > 0) & (s < 10))),
("one", "bar"): Column(
DateTime, Check(lambda s: s == pd.Timestamp(2019, 1, 1))
),
}
)
validated_df = schema.validate(
pd.DataFrame(
{
("zero", "foo"): [0.1, 0.2, 0.7, 0.3],
("zero", "bar"): ["a", "b", "c", "d"],
("one", "foo"): [1, 6, 4, 7],
("one", "bar"): pd.to_datetime(["2019/01/01"] * 4),
}
)
)
assert isinstance(validated_df, pd.DataFrame)
def test_required():
"""Tests how a Required Column is handled when it's not included, included
and then not specified and a second column which is implicitly required
isn't available."""
schema = DataFrameSchema({
"col1": Column(Int, required=False),
"col2": Column(String)
})
df_ok_1 = pd.DataFrame({
"col2": ['hello', 'world']
})
df = schema.validate(df_ok_1)
assert isinstance(df, pd.DataFrame)
assert len(df.columns) == 1
assert set(df.columns) == {"col2"}
df_ok_2 = pd.DataFrame({
"col1": [1, 2],
"col2": ['hello', 'world']
})
df = schema.validate(df_ok_2)
assert isinstance(df, pd.DataFrame)
assert len(df.columns) == 2
assert set(df.columns) == {"col1", "col2"}
df_not_ok = pd.DataFrame({
"col1": [1, 2]
})
with pytest.raises(Exception):
schema.validate(df_not_ok)
def test_lazy_dataframe_validation_nullable():
"""
Test that non-nullable column failure cases are correctly processed during
lazy validation.
"""
schema = DataFrameSchema(
columns={
"int_column": Column(Int, nullable=False),
"float_column": Column(Float, nullable=False),
"str_column": Column(String, nullable=False),
},
strict=True,
)
df = pd.DataFrame({
"int_column": [1, None, 3],
"float_column": [0.1, 1.2, None],
"str_column": [None, "foo", "bar"],
})
try:
schema.validate(df, lazy=True)
except errors.SchemaErrors as err:
assert err.failure_cases.failure_case.isna().all()
for col, index in [
("int_column", 1),
("float_column", 2),
("str_column", 0),
]:
# pylint: disable=cell-var-from-loop
assert (err.failure_cases.loc[lambda df: df.column == col,
"index"].iloc[0] == index)
def test_coerce_dtype_nullable_str():
"""Tests how null values are handled in string dtypes."""
# dataframes with columns where the last two values are null
df_nans = pd.DataFrame({
"col": ["foobar", "foo", "bar", "baz", np.nan, np.nan],
})
df_nones = pd.DataFrame({
"col": ["foobar", "foo", "bar", "baz", None, None],
})
with pytest.raises(errors.SchemaError):
for df in [df_nans, df_nones]:
DataFrameSchema({
"col": Column(String, coerce=True, nullable=False)
}).validate(df)
schema = DataFrameSchema({
"col": Column(String, coerce=True, nullable=True)
})
for df in [df_nans, df_nones]:
validated_df = schema.validate(df)
assert isinstance(validated_df, pd.DataFrame)
assert pd.isna(validated_df["col"].iloc[-1])
assert pd.isna(validated_df["col"].iloc[-2])
for i in range(4):
assert isinstance(validated_df["col"].iloc[i], str)
def test_multi_index_index():
schema = DataFrameSchema(
columns={
"column1": Column(Float, Check(lambda s: s > 0)),
"column2": Column(Float, Check(lambda s: s > 0)),
},
index=MultiIndex(indexes=[
Index(Int, Check(lambda s: (s < 5) & (s >= 0)), name="index0"),
Index(String,
Check(lambda s: s.isin(["foo", "bar"])),
name="index1"),
]))
df = pd.DataFrame(
data={
"column1": [0.1, 0.5, 123.1, 10.6, 22.31],
"column2": [0.1, 0.5, 123.1, 10.6, 22.31],
},
index=pd.MultiIndex.from_arrays(
[[0, 1, 2, 3, 4], ["foo", "bar", "foo", "bar", "foo"]],
names=["index0", "index1"],
))
validated_df = schema.validate(df)
assert isinstance(validated_df, pd.DataFrame)
# failure case
df_fail = df.copy()
df_fail.index = pd.MultiIndex.from_arrays(
[[-1, 1, 2, 3, 4], ["foo", "bar", "foo", "bar", "foo"]],
names=["index0", "index1"],
)
with pytest.raises(errors.SchemaError):
schema.validate(df_fail)
def test_check_groupby():
"""Tests uses of groupby to specify dependencies between one column and a
single other column, including error handling."""
schema = DataFrameSchema(
columns={
"col1":
Column(Int, [
Check(lambda s: s["foo"] > 10, groupby="col2"),
Check(lambda s: s["bar"] < 10, groupby=["col2"]),
Check(lambda s: s["foo"] > 10,
groupby=lambda df: df.groupby("col2")),
Check(lambda s: s["bar"] < 10,
groupby=lambda df: df.groupby("col2"))
]),
"col2":
Column(String, Check(lambda s: s.isin(["foo", "bar"]))),
},
index=Index(Int, name="data_id"),
)
df_pass = pd.DataFrame(
data={
"col1": [7, 8, 9, 11, 12, 13],
"col2": ["bar", "bar", "bar", "foo", "foo", "foo"],
},
index=pd.Series([1, 2, 3, 4, 5, 6], name="data_id"),
)
df = schema.validate(df_pass)
assert isinstance(df, pd.DataFrame)
assert len(df.columns) == 2
assert set(df.columns) == {"col1", "col2"}
# raise errors.SchemaError when Check fails
df_fail_on_bar = pd.DataFrame(
data={
"col1": [7, 8, 20, 11, 12, 13],
"col2": ["bar", "bar", "bar", "foo", "foo", "foo"],
},
index=pd.Series([1, 2, 3, 4, 5, 6], name="data_id"),
)
df_fail_on_foo = pd.DataFrame(
data={
"col1": [7, 8, 9, 11, 1, 13],
"col2": ["bar", "bar", "bar", "foo", "foo", "foo"],
},
index=pd.Series([1, 2, 3, 4, 5, 6], name="data_id"),
)
# raise errors.SchemaError when groupby column doesn't exist
df_fail_no_column = pd.DataFrame(
data={
"col1": [7, 8, 20, 11, 12, 13],
},
index=pd.Series([1, 2, 3, 4, 5, 6], name="data_id"),
)
for df in [df_fail_on_bar, df_fail_on_foo, df_fail_no_column]:
with pytest.raises(errors.SchemaError):
schema.validate(df)
def test_column_regex_matching_non_str_types(
column_name_regex: str, expected_matches: List
) -> None:
"""Non-string column names should be cast into str for regex matching."""
columns = pd.Index([1, 2.2, 3.1415, -1, -3.6, pd.Timestamp("2018/01/01")])
column_schema = Column(name=column_name_regex, regex=True)
matched_columns = column_schema.get_regex_columns(columns)
assert expected_matches == matched_columns.tolist()
def test_coerce_without_dtype():
"""Test that an error is thrown when a dtype isn't specified and coerce
is True."""
with pytest.raises(errors.SchemaInitError):
DataFrameSchema({"col": Column(coerce=True)})
with pytest.raises(errors.SchemaInitError):
DataFrameSchema({"col": Column()}, coerce=True)
def test_dataframe_hypothesis_checks():
df = pd.DataFrame({
"col1": range(100, 201),
"col2": range(0, 101),
})
hypothesis_check_schema = DataFrameSchema(
columns={
"col1": Column(Int),
"col2": Column(Int),
},
checks=[
# two-sample test
Hypothesis(
test=stats.ttest_ind,
samples=["col1", "col2"],
relationship=lambda stat, pvalue, alpha=0.01: (
stat > 0 and pvalue / 2 < alpha
),
relationship_kwargs={"alpha": 0.5},
),
# one-sample test
Hypothesis(
test=stats.ttest_1samp,
samples=["col1"],
relationship=lambda stat, pvalue, alpha=0.01: (
stat > 0 and pvalue / 2 < alpha
),
test_kwargs={"popmean": 50},
relationship_kwargs={"alpha": 0.01},
),
]
)
hypothesis_check_schema.validate(df)
# raise error when using groupby
hypothesis_check_schema_groupby = DataFrameSchema(
columns={
"col1": Column(Int),
"col2": Column(Int),
},
checks=[
# two-sample test
Hypothesis(
test=stats.ttest_ind,
samples=["col1", "col2"],
groupby="col3",
relationship=lambda stat, pvalue, alpha=0.01: (
stat > 0 and pvalue / 2 < alpha
),
relationship_kwargs={"alpha": 0.5},
),
]
)
with pytest.raises(errors.SchemaDefinitionError):
hypothesis_check_schema_groupby.validate(df)
def test_dataframe_hypothesis_checks():
"""
Test that two specific implementations of a Hypothesis work as expected
and that using a Column that wasn't defined will error.
"""
df = pd.DataFrame({
"col1": range(100, 201),
"col2": range(0, 101),
})
hypothesis_check_schema = DataFrameSchema(
columns={
"col1": Column(Int),
"col2": Column(Int),
},
checks=[
# two-sample test
Hypothesis(
test=stats.ttest_ind,
samples=["col1", "col2"],
relationship=lambda stat, pvalue, alpha=0.01:
(stat > 0 and pvalue / 2 < alpha),
relationship_kwargs={"alpha": 0.5},
),
# one-sample test
Hypothesis(
test=stats.ttest_1samp,
samples=["col1"],
relationship=lambda stat, pvalue, alpha=0.01:
(stat > 0 and pvalue / 2 < alpha),
test_kwargs={"popmean": 50},
relationship_kwargs={"alpha": 0.01},
),
],
)
hypothesis_check_schema.validate(df)
# raise error when using groupby for a column that doesn't exist
hypothesis_check_schema_groupby = DataFrameSchema(
columns={
"col1": Column(Int),
"col2": Column(Int),
},
checks=[
# two-sample test
Hypothesis(
test=stats.ttest_ind,
samples=["col1", "col2"],
groupby="col3",
relationship=lambda stat, pvalue, alpha=0.01:
(stat > 0 and pvalue / 2 < alpha),
relationship_kwargs={"alpha": 0.5},
),
],
)
with pytest.raises(errors.SchemaDefinitionError):
hypothesis_check_schema_groupby.validate(df)
def plot_roc_auc(y_true, y_pred, label, ax=None):
fpr, tpr, _ = roc_curve(y_true, y_pred)
roc_curve_df = pd.DataFrame({"fpr": fpr, "tpr": tpr}).pipe(
pa.DataFrameSchema({
"fpr": Column(pa.Float, Check.in_range(0, 1)),
"tpr": Column(pa.Float, Check.in_range(0, 1)),
})
)
return roc_curve_df.plot.line(x="fpr", y="tpr", label=label, ax=ax)
def init_schema_element_wise():
DataFrameSchema({
"col1": Column(Int, [
Check(lambda s: s["foo"] > 10,
element_wise=True,
groupby=["col2"]),
]),
"col2": Column(String, Check(lambda s: s.isin(["foo", "bar"]))),
})
def schema_simple():
"""Simple schema fixture."""
schema = DataFrameSchema(
columns={
"col1": Column(pandas_dtype=Int),
"col2": Column(pandas_dtype=Float),
},
index=Index(pandas_dtype=String, name="ind0"),
)
return schema
def test_add_and_remove_columns():
"""Check that adding and removing columns works as expected and doesn't
modify the original underlying DataFrameSchema."""
schema1 = DataFrameSchema(
{
"col1": Column(Int, Check(lambda s: s >= 0)),
},
strict=True,
)
schema1_exact_copy = copy.deepcopy(schema1)
# test that add_columns doesn't modify schema1 after add_columns:
schema2 = schema1.add_columns(
{
"col2": Column(String, Check(lambda x: x <= 0)),
"col3": Column(Object, Check(lambda x: x == 0)),
}
)
schema2_exact_copy = copy.deepcopy(schema2)
assert schema1 == schema1_exact_copy
# test that add_columns changed schema1 into schema2:
expected_schema_2 = DataFrameSchema(
{
"col1": Column(Int, Check(lambda s: s >= 0)),
"col2": Column(String, Check(lambda x: x <= 0)),
"col3": Column(Object, Check(lambda x: x == 0)),
},
strict=True,
)
assert schema2 == expected_schema_2
# test that remove_columns doesn't modify schema2:
schema3 = schema2.remove_columns(["col2"])
assert schema2 == schema2_exact_copy
# test that remove_columns has removed the changes as expected:
expected_schema_3 = DataFrameSchema(
{
"col1": Column(Int, Check(lambda s: s >= 0)),
"col3": Column(Object, Check(lambda x: x == 0)),
},
strict=True,
)
assert schema3 == expected_schema_3
# test that remove_columns can remove two columns:
schema4 = schema2.remove_columns(["col2", "col3"])
expected_schema_4 = DataFrameSchema(
{"col1": Column(Int, Check(lambda s: s >= 0))}, strict=True
)
assert schema4 == expected_schema_4 == schema1
def test_no_dtype_dataframe():
schema = DataFrameSchema({"col": Column(nullable=False)})
validated_df = schema.validate(pd.DataFrame({"col": [-123.1, -76.3, 1.0]}))
assert isinstance(validated_df, pd.DataFrame)
schema = DataFrameSchema({"col": Column(nullable=True)})
validated_df = schema.validate(pd.DataFrame({"col": [-123.1, None, 1.0]}))
assert isinstance(validated_df, pd.DataFrame)
with pytest.raises(errors.SchemaError):
schema = DataFrameSchema({"col": Column(nullable=False)})
schema.validate(pd.DataFrame({"col": [-123.1, None, 1.0]}))
def test_check_input_method_decorators():
"""Test the check_input and check_output decorator behaviours when the
dataframe is changed within the function being checked"""
in_schema = DataFrameSchema({"column1": Column(String)})
out_schema = DataFrameSchema({"column2": Column(Int)})
dataframe = pd.DataFrame({"column1": ["a", "b", "c"]})
def _transform_helper(df):
return df.assign(column2=[1, 2, 3])
class TransformerClass():
"""Contains functions with different signatures representing the way
that the decorators can be called."""
# pylint: disable=E0012,C0111,C0116,W0613, R0201
# disables missing-function-docstring as this is a factory method
# disables unused-arguments because handling the second argument is
# what is being tested and this is intentional.
# disables no-self-use because having TransformerClass with functions
# is cleaner.
@check_input(in_schema)
@check_output(out_schema)
def transform_first_arg(self, df):
return _transform_helper(df)
@check_input(in_schema, 0)
@check_output(out_schema)
def transform_first_arg_with_list_getter(self, df):
return _transform_helper(df)
@check_input(in_schema, 1)
@check_output(out_schema)
def transform_secord_arg_with_list_getter(self, x, df):
return _transform_helper(df)
@check_input(in_schema, "df")
@check_output(out_schema)
def transform_secord_arg_with_dict_getter(self, x, df):
return _transform_helper(df)
def _assert_expectation(result_df):
assert isinstance(result_df, pd.DataFrame)
assert "column2" in result_df.columns
transformer = TransformerClass()
_assert_expectation(transformer.transform_first_arg(dataframe))
_assert_expectation(
transformer.transform_first_arg_with_list_getter(dataframe))
_assert_expectation(
transformer.transform_secord_arg_with_list_getter(None, dataframe))
_assert_expectation(
transformer.transform_secord_arg_with_dict_getter(None, dataframe))
def schema_multiindex():
"""Fixture for schema with MultiIndex."""
schema = DataFrameSchema(
columns={
"col1": Column(pandas_dtype=Int),
"col2": Column(pandas_dtype=Float),
},
index=MultiIndex([
Index(pandas_dtype=String, name="ind0"),
Index(pandas_dtype=String, name="ind1"),
]),
)
return schema
def test_dataframe_schema_check_function_types(check_function, should_fail):
schema = DataFrameSchema({
"a":
Column(Int, Check(fn=check_function, element_wise=False)),
"b":
Column(Float, Check(fn=check_function, element_wise=False))
})
df = pd.DataFrame({"a": [1, 2, 3], "b": [1.1, 2.5, 9.9]})
if should_fail:
with pytest.raises(errors.SchemaError):
schema.validate(df)
else:
schema.validate(df)
