def test_null_is_not_a_category(self):
# pd.CategoricalDtype means storing nulls as -1. Don't consider -1 when
# counting the used categories.
with self.assertRaisesRegex(ValueError, "unused category 'b'"):
validate_dataframe(
pd.DataFrame({"foo": ["a", None]},
dtype=pd.CategoricalDtype(["a", "b"])))
def test_unsupported_numpy_dtype_unsupported(self):
# We can't check if a numpy dtype == 'category'.
# https://github.com/pandas-dev/pandas/issues/16697
arr = np.array([1, 2, 3]).astype("complex") # we don't support complex
dataframe = pd.DataFrame({"A": arr})
with self.assertRaisesRegex(ValueError, "unsupported dtype"):
validate_dataframe(dataframe)
def test_unsupported_dtype(self):
dataframe = pd.DataFrame({
# A type we never plan on supporting
"A":
pd.Series([pd.Interval(0, 1)], dtype="interval")
})
with self.assertRaisesRegex(ValueError, "unsupported dtype"):
validate_dataframe(dataframe)
def test_datetime64tz_unsupported(self):
dataframe = pd.DataFrame({
# We don't support datetimes with time zone data ... yet
"A":
pd.Series([pd.to_datetime("2019-04-23T12:34:00-0500")])
})
with self.assertRaisesRegex(ValueError, "unsupported dtype"):
validate_dataframe(dataframe)
def test_nullable_int_unsupported(self):
dataframe = pd.DataFrame({
# We don't support nullable integer columns ... yet
"A":
pd.Series([1, np.nan], dtype=pd.Int64Dtype())
})
with self.assertRaisesRegex(ValueError, "unsupported dtype"):
validate_dataframe(dataframe)
def test_infinity_not_supported(self):
# Make 'A': [1, -inf, +inf, nan]
num = pd.Series([1, -2, 3, np.nan])
denom = pd.Series([1, 0, 0, 1])
dataframe = pd.DataFrame({"A": num / denom})
with self.assertRaisesRegex(
ValueError,
("invalid value -inf in column 'A', row 1 "
"\(infinity is not supported\)"),
):
validate_dataframe(dataframe)
def test_colnames_all_str(self):
with self.assertRaisesRegex(ValueError, "column names"):
# df.columns is object, but not all are str
validate_dataframe(pd.DataFrame({"A": [1], 2: [2]}))
def test_empty_categories(self):
df = pd.DataFrame({"A": []}, dtype="category")
validate_dataframe(df)
def test_non_str_objects(self):
with self.assertRaisesRegex(ValueError, "must all be str"):
validate_dataframe(pd.DataFrame({"foo": ["a", 1]}))
def test_index(self):
with self.assertRaisesRegex(ValueError,
"must use the default RangeIndex"):
validate_dataframe(pd.DataFrame({"A": [1, 2]})[1:])
def test_numpy_dtype(self):
# Numpy dtypes should be treated just like pandas dtypes.
dataframe = pd.DataFrame({"A": np.array([1, 2, 3])})
validate_dataframe(dataframe)
def test_empty_categories_with_wrong_dtype(self):
with self.assertRaisesRegex(ValueError, "must have dtype=object"):
validate_dataframe(
pd.DataFrame({
"foo": [np.nan]
}, dtype=float).astype("category"))
def test_empty_colname(self):
dataframe = pd.DataFrame({"": [1], "B": [2]})
with self.assertRaisesRegex(ValueError, "empty column name"):
validate_dataframe(dataframe)
def test_unique_colnames(self):
dataframe = pd.DataFrame({"A": [1], "B": [2]})
dataframe.columns = ["A", "A"]
with self.assertRaisesRegex(ValueError, "duplicate column name"):
validate_dataframe(dataframe)
def test_colnames_dtype_object(self):
with self.assertRaisesRegex(ValueError, "column names"):
# df.columns is numeric
validate_dataframe(pd.DataFrame({1: [1]}))
def test_non_str_categories(self):
with self.assertRaisesRegex(ValueError, "must all be str"):
validate_dataframe(
pd.DataFrame({"foo": ["a", 1]}, dtype="category"))
def test_unused_categories(self):
with self.assertRaisesRegex(ValueError, "unused category 'b'"):
validate_dataframe(
pd.DataFrame({"foo": ["a", "a"]},
dtype=pd.CategoricalDtype(["a", "b"])))