def test_categories_to_string_allows_abnormal_index(self):
class Obj:
def __init__(self, value):
self.value = value
def __str__(self):
return self.value
# Slicing a Series means the category list remains complete, even
# though some categories aren't used. In this example, `series` has an
# Obj('a') category, even though the value doesn't appear anywhere in
# the dataframe. (This is because slicing creates a numpy "view", not a
# copy of the original array of codes.)
#
# Sanitize's output shouldn't include any categories that aren't
# visible. (The data in memory should not be a "view".)
#
# Also, sanitize_series() should reset the index.
series = pd.Series([Obj("a"), Obj("b"), "c", "b"],
dtype="category")[1:]
result = sanitize_series(series)
expected = pd.Series(["b", "c", "b"], dtype="category")
assert_series_equal(result, expected)
# to reiterate: 'result' has no category that looks like 'a'.
self.assertEqual(sorted(result.cat.categories.tolist()), ["b", "c"])
def test_mixed_to_string_keeps_nan(self):
# check that sanitizing a non-string column with missing data produces
# empty cells, not 'nan' strings
# https://www.pivotaltracker.com/story/show/154619564
series = pd.Series([1.0, "str", np.nan, ""]) # mixed
result = sanitize_series(series)
assert_series_equal(result, pd.Series(["1.0", "str", np.nan, ""]))
def test_mixed_to_string_allows_custom_types(self):
class Obj:
def __str__(self):
return "x"
series = pd.Series([Obj(), Obj()])
result = sanitize_series(series)
expected = pd.Series(["x", "x"])
assert_series_equal(result, expected)
def test_remove_unused_categories(self):
series = pd.Series(
["a", "b"],
# extraneous value
dtype=pd.api.types.CategoricalDtype(["a", "b", "c"]),
)
result = sanitize_series(series)
expected = pd.Series(["a", "b"], dtype="category")
assert_series_equal(result, expected)
def test_categories_to_string_allows_custom_category_types(self):
class Obj:
def __init__(self, value):
self.value = value
def __str__(self):
return self.value
series = pd.Series(
[Obj("a"), Obj("b"), Obj("a"), "a", "y"], dtype="category")
result = sanitize_series(series)
expected = pd.Series(["a", "b", "a", "a", "y"], dtype="category")
assert_series_equal(result, expected)
def test_cast_mixed_category_to_str(self):
series = pd.Series([1, "2"], dtype="category")
result = sanitize_series(series)
expected = pd.Series(["1", "2"], dtype="category")
assert_series_equal(result, expected)
def test_cast_int_category_to_int(self):
series = pd.Series([1, 2], dtype="category")
result = sanitize_series(series)
assert_series_equal(result, pd.Series([1, 2]))
def test_reset_index(self):
# should always come out with row numbers contiguous from zero
series = pd.Series([1, 2, 3])[1:]
result = sanitize_series(series)
assert_series_equal(result, pd.Series([2, 3])) # index is [0,1]
def test_lists_and_dicts(self):
series = pd.Series([[5, 6, 7], {"a": "b"}])
result = sanitize_series(series)
expected = pd.Series(["[5, 6, 7]", "{'a': 'b'}"])
assert_series_equal(result, expected)
