/usr/local/lib64/python3.6/site-packages/pandas/tests/arrays/categorical
Edit: /usr/local/lib64/python3.6/site-packages/pandas/tests/arrays/categorical/test_missing.py (5589B)
import collections
import numpy as np
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import Categorical, DataFrame, Index, Series, isna
import pandas._testing as tm
class TestCategoricalMissing:
def test_na_flags_int_categories(self):
# #1457
categories = list(range(10))
labels = np.random.randint(0, 10, 20)
labels[::5] = -1
cat = Categorical(labels, categories, fastpath=True)
repr(cat)
tm.assert_numpy_array_equal(isna(cat), labels == -1)
def test_nan_handling(self):
# Nans are represented as -1 in codes
c = Categorical(["a", "b", np.nan, "a"])
tm.assert_index_equal(c.categories, Index(["a", "b"]))
tm.assert_numpy_array_equal(c._codes, np.array([0, 1, -1, 0], dtype=np.int8))
c[1] = np.nan
tm.assert_index_equal(c.categories, Index(["a", "b"]))
tm.assert_numpy_array_equal(c._codes, np.array([0, -1, -1, 0], dtype=np.int8))
# Adding nan to categories should make assigned nan point to the
# category!
c = Categorical(["a", "b", np.nan, "a"])
tm.assert_index_equal(c.categories, Index(["a", "b"]))
tm.assert_numpy_array_equal(c._codes, np.array([0, 1, -1, 0], dtype=np.int8))
def test_set_dtype_nans(self):
c = Categorical(["a", "b", np.nan])
result = c._set_dtype(CategoricalDtype(["a", "c"]))
tm.assert_numpy_array_equal(result.codes, np.array([0, -1, -1], dtype="int8"))
def test_set_item_nan(self):
cat = Categorical([1, 2, 3])
cat[1] = np.nan
exp = Categorical([1, np.nan, 3], categories=[1, 2, 3])
tm.assert_categorical_equal(cat, exp)
@pytest.mark.parametrize(
"fillna_kwargs, msg",
[
(
dict(value=1, method="ffill"),
"Cannot specify both 'value' and 'method'.",
),
(dict(), "Must specify a fill 'value' or 'method'."),
(dict(method="bad"), "Invalid fill method. Expecting .* bad"),
(dict(value=Series([1, 2, 3, 4, "a"])), "fill value must be in categories"),
],
)
def test_fillna_raises(self, fillna_kwargs, msg):
# https://github.com/pandas-dev/pandas/issues/19682
# https://github.com/pandas-dev/pandas/issues/13628
cat = Categorical([1, 2, 3, None, None])
with pytest.raises(ValueError, match=msg):
cat.fillna(**fillna_kwargs)
@pytest.mark.parametrize("named", [True, False])
def test_fillna_iterable_category(self, named):
# https://github.com/pandas-dev/pandas/issues/21097
if named:
Point = collections.namedtuple("Point", "x y")
else:
Point = lambda *args: args # tuple
cat = Categorical(np.array([Point(0, 0), Point(0, 1), None], dtype=object))
result = cat.fillna(Point(0, 0))
expected = Categorical([Point(0, 0), Point(0, 1), Point(0, 0)])
tm.assert_categorical_equal(result, expected)
def test_fillna_array(self):
# accept Categorical or ndarray value if it holds appropriate values
cat = Categorical(["A", "B", "C", None, None])
other = cat.fillna("C")
result = cat.fillna(other)
tm.assert_categorical_equal(result, other)
assert isna(cat[-1]) # didnt modify original inplace
other = np.array(["A", "B", "C", "B", "A"])
result = cat.fillna(other)
expected = Categorical(["A", "B", "C", "B", "A"], dtype=cat.dtype)
tm.assert_categorical_equal(result, expected)
assert isna(cat[-1]) # didnt modify original inplace
@pytest.mark.parametrize(
"values, expected",
[
([1, 2, 3], np.array([False, False, False])),
([1, 2, np.nan], np.array([False, False, True])),
([1, 2, np.inf], np.array([False, False, True])),
([1, 2, pd.NA], np.array([False, False, True])),
],
)
def test_use_inf_as_na(self, values, expected):
# https://github.com/pandas-dev/pandas/issues/33594
with pd.option_context("mode.use_inf_as_na", True):
cat = Categorical(values)
result = cat.isna()
tm.assert_numpy_array_equal(result, expected)
result = Series(cat).isna()
expected = Series(expected)
tm.assert_series_equal(result, expected)
result = DataFrame(cat).isna()
expected = DataFrame(expected)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"values, expected",
[
([1, 2, 3], np.array([False, False, False])),
([1, 2, np.nan], np.array([False, False, True])),
([1, 2, np.inf], np.array([False, False, True])),
([1, 2, pd.NA], np.array([False, False, True])),
],
)
def test_use_inf_as_na_outside_context(self, values, expected):
# https://github.com/pandas-dev/pandas/issues/33594
# Using isna directly for Categorical will fail in general here
cat = Categorical(values)
with pd.option_context("mode.use_inf_as_na", True):
result = pd.isna(cat)
tm.assert_numpy_array_equal(result, expected)
result = pd.isna(Series(cat))
expected = Series(expected)
tm.assert_series_equal(result, expected)
result = pd.isna(DataFrame(cat))
expected = DataFrame(expected)
tm.assert_frame_equal(result, expected)