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import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
@pytest.mark.parametrize("ordered", [True, False])
@pytest.mark.parametrize("categories", [["b", "a", "c"], ["a", "b", "c", "d"]])
def test_factorize(categories, ordered):
cat = pd.Categorical(
["b", "b", "a", "c", None], categories=categories, ordered=ordered
)
codes, uniques = pd.factorize(cat)
expected_codes = np.array([0, 0, 1, 2, -1], dtype=np.intp)
expected_uniques = pd.Categorical(
["b", "a", "c"], categories=categories, ordered=ordered
)
tm.assert_numpy_array_equal(codes, expected_codes)
tm.assert_categorical_equal(uniques, expected_uniques)
def test_factorized_sort():
cat = pd.Categorical(["b", "b", None, "a"])
codes, uniques = pd.factorize(cat, sort=True)
expected_codes = np.array([1, 1, -1, 0], dtype=np.intp)
expected_uniques = pd.Categorical(["a", "b"])
tm.assert_numpy_array_equal(codes, expected_codes)
tm.assert_categorical_equal(uniques, expected_uniques)
def test_factorized_sort_ordered():
cat = pd.Categorical(
["b", "b", None, "a"], categories=["c", "b", "a"], ordered=True
)
codes, uniques = pd.factorize(cat, sort=True)
expected_codes = np.array([0, 0, -1, 1], dtype=np.intp)
expected_uniques = pd.Categorical(
["b", "a"], categories=["c", "b", "a"], ordered=True
)
tm.assert_numpy_array_equal(codes, expected_codes)
tm.assert_categorical_equal(uniques, expected_uniques)
def test_isin_cats():
# GH2003
cat = pd.Categorical(["a", "b", np.nan])
result = cat.isin(["a", np.nan])
expected = np.array([True, False, True], dtype=bool)
tm.assert_numpy_array_equal(expected, result)
result = cat.isin(["a", "c"])
expected = np.array([True, False, False], dtype=bool)
tm.assert_numpy_array_equal(expected, result)
@pytest.mark.parametrize(
"to_replace, value, result, expected_error_msg",
[
("b", "c", ["a", "c"], "Categorical.categories are different"),
("c", "d", ["a", "b"], None),
# https://github.com/pandas-dev/pandas/issues/33288
("a", "a", ["a", "b"], None),
("b", None, ["a", None], "Categorical.categories length are different"),
],
)
def test_replace(to_replace, value, result, expected_error_msg):
# GH 26988
cat = pd.Categorical(["a", "b"])
expected = pd.Categorical(result)
result = cat.replace(to_replace, value)
tm.assert_categorical_equal(result, expected)
if to_replace == "b": # the "c" test is supposed to be unchanged
with pytest.raises(AssertionError, match=expected_error_msg):
# ensure non-inplace call does not affect original
tm.assert_categorical_equal(cat, expected)
cat.replace(to_replace, value, inplace=True)
tm.assert_categorical_equal(cat, expected)
@pytest.mark.parametrize("empty", [[], pd.Series(dtype=object), np.array([])])
def test_isin_empty(empty):
s = pd.Categorical(["a", "b"])
expected = np.array([False, False], dtype=bool)
result = s.isin(empty)
tm.assert_numpy_array_equal(expected, result)
def test_diff():
s = pd.Series([1, 2, 3], dtype="category")
with tm.assert_produces_warning(FutureWarning):
result = s.diff()
expected = pd.Series([np.nan, 1, 1])
tm.assert_series_equal(result, expected)
expected = expected.to_frame(name="A")
df = s.to_frame(name="A")
with tm.assert_produces_warning(FutureWarning):
result = df.diff()
tm.assert_frame_equal(result, expected)
class TestTake:
# https://github.com/pandas-dev/pandas/issues/20664
def test_take_default_allow_fill(self):
cat = pd.Categorical(["a", "b"])
with tm.assert_produces_warning(None):
result = cat.take([0, -1])
assert result.equals(cat)
def test_take_positive_no_warning(self):
cat = pd.Categorical(["a", "b"])
with tm.assert_produces_warning(None):
cat.take([0, 0])
def test_take_bounds(self, allow_fill):
# https://github.com/pandas-dev/pandas/issues/20664
cat = pd.Categorical(["a", "b", "a"])
if allow_fill:
msg = "indices are out-of-bounds"
else:
msg = "index 4 is out of bounds for( axis 0 with)? size 3"
with pytest.raises(IndexError, match=msg):
cat.take([4, 5], allow_fill=allow_fill)
def test_take_empty(self, allow_fill):
# https://github.com/pandas-dev/pandas/issues/20664
cat = pd.Categorical([], categories=["a", "b"])
if allow_fill:
msg = "indices are out-of-bounds"
else:
msg = "cannot do a non-empty take from an empty axes"
with pytest.raises(IndexError, match=msg):
cat.take([0], allow_fill=allow_fill)
def test_positional_take(self, ordered):
cat = pd.Categorical(
["a", "a", "b", "b"], categories=["b", "a"], ordered=ordered
)
result = cat.take([0, 1, 2], allow_fill=False)
expected = pd.Categorical(
["a", "a", "b"], categories=cat.categories, ordered=ordered
)
tm.assert_categorical_equal(result, expected)
def test_positional_take_unobserved(self, ordered):
cat = pd.Categorical(["a", "b"], categories=["a", "b", "c"], ordered=ordered)
result = cat.take([1, 0], allow_fill=False)
expected = pd.Categorical(
["b", "a"], categories=cat.categories, ordered=ordered
)
tm.assert_categorical_equal(result, expected)
def test_take_allow_fill(self):
# https://github.com/pandas-dev/pandas/issues/23296
cat = pd.Categorical(["a", "a", "b"])
result = cat.take([0, -1, -1], allow_fill=True)
expected = pd.Categorical(["a", np.nan, np.nan], categories=["a", "b"])
tm.assert_categorical_equal(result, expected)
def test_take_fill_with_negative_one(self):
# -1 was a category
cat = pd.Categorical([-1, 0, 1])
result = cat.take([0, -1, 1], allow_fill=True, fill_value=-1)
expected = pd.Categorical([-1, -1, 0], categories=[-1, 0, 1])
tm.assert_categorical_equal(result, expected)
def test_take_fill_value(self):
# https://github.com/pandas-dev/pandas/issues/23296
cat = pd.Categorical(["a", "b", "c"])
result = cat.take([0, 1, -1], fill_value="a", allow_fill=True)
expected = pd.Categorical(["a", "b", "a"], categories=["a", "b", "c"])
tm.assert_categorical_equal(result, expected)
def test_take_fill_value_new_raises(self):
# https://github.com/pandas-dev/pandas/issues/23296
cat = pd.Categorical(["a", "b", "c"])
xpr = r"'fill_value=d' is not present in this Categorical's categories"
with pytest.raises(ValueError, match=xpr):
cat.take([0, 1, -1], fill_value="d", allow_fill=True)
def test_take_nd_deprecated(self):
cat = pd.Categorical(["a", "b", "c"])
with tm.assert_produces_warning(FutureWarning):
cat.take_nd([0, 1])
ci = pd.Index(cat)
with tm.assert_produces_warning(FutureWarning):
ci.take_nd([0, 1])