Split test_categorical into subpackage (#18497)

pandas/tests/categorical/common.py

@@ -0,0 +1,10 @@
+# -*- coding: utf-8 -*-
+
+from pandas import Categorical
+
+
+class TestCategorical(object):
+
+    def setup_method(self, method):
+        self.factor = Categorical(['a', 'b', 'b', 'a', 'a', 'c', 'c', 'c'],
+                                  ordered=True)

pandas/tests/categorical/test_analytics.py

@@ -0,0 +1,320 @@
+# -*- coding: utf-8 -*-
+
+import pytest
+import sys
+
+import numpy as np
+
+import pandas.util.testing as tm
+from pandas import Categorical, Index, Series
+
+from pandas.compat import PYPY
+
+
+class TestCategoricalAnalytics(object):
+
+    def test_min_max(self):
+
+        # unordered cats have no min/max
+        cat = Categorical(["a", "b", "c", "d"], ordered=False)
+        pytest.raises(TypeError, lambda: cat.min())
+        pytest.raises(TypeError, lambda: cat.max())
+
+        cat = Categorical(["a", "b", "c", "d"], ordered=True)
+        _min = cat.min()
+        _max = cat.max()
+        assert _min == "a"
+        assert _max == "d"
+
+        cat = Categorical(["a", "b", "c", "d"],
+                          categories=['d', 'c', 'b', 'a'], ordered=True)
+        _min = cat.min()
+        _max = cat.max()
+        assert _min == "d"
+        assert _max == "a"
+
+        cat = Categorical([np.nan, "b", "c", np.nan],
+                          categories=['d', 'c', 'b', 'a'], ordered=True)
+        _min = cat.min()
+        _max = cat.max()
+        assert np.isnan(_min)
+        assert _max == "b"
+
+        _min = cat.min(numeric_only=True)
+        assert _min == "c"
+        _max = cat.max(numeric_only=True)
+        assert _max == "b"
+
+        cat = Categorical([np.nan, 1, 2, np.nan], categories=[5, 4, 3, 2, 1],
+                          ordered=True)
+        _min = cat.min()
+        _max = cat.max()
+        assert np.isnan(_min)
+        assert _max == 1
+
+        _min = cat.min(numeric_only=True)
+        assert _min == 2
+        _max = cat.max(numeric_only=True)
+        assert _max == 1
+
+    @pytest.mark.parametrize("values,categories,exp_mode", [
+        ([1, 1, 2, 4, 5, 5, 5], [5, 4, 3, 2, 1], [5]),
+        ([1, 1, 1, 4, 5, 5, 5], [5, 4, 3, 2, 1], [5, 1]),
+        ([1, 2, 3, 4, 5], [5, 4, 3, 2, 1], [5, 4, 3, 2, 1]),
+        ([np.nan, np.nan, np.nan, 4, 5], [5, 4, 3, 2, 1], [5, 4]),
+        ([np.nan, np.nan, np.nan, 4, 5, 4], [5, 4, 3, 2, 1], [4]),
+        ([np.nan, np.nan, 4, 5, 4], [5, 4, 3, 2, 1], [4])])
+    def test_mode(self, values, categories, exp_mode):
+        s = Categorical(values, categories=categories, ordered=True)
+        res = s.mode()
+        exp = Categorical(exp_mode, categories=categories, ordered=True)
+        tm.assert_categorical_equal(res, exp)
+
+    def test_searchsorted(self):
+        # https://github.com/pandas-dev/pandas/issues/8420
+        # https://github.com/pandas-dev/pandas/issues/14522
+
+        c1 = Categorical(['cheese', 'milk', 'apple', 'bread', 'bread'],
+                         categories=['cheese', 'milk', 'apple', 'bread'],
+                         ordered=True)
+        s1 = Series(c1)
+        c2 = Categorical(['cheese', 'milk', 'apple', 'bread', 'bread'],
+                         categories=['cheese', 'milk', 'apple', 'bread'],
+                         ordered=False)
+        s2 = Series(c2)
+
+        # Searching for single item argument, side='left' (default)
+        res_cat = c1.searchsorted('apple')
+        res_ser = s1.searchsorted('apple')
+        exp = np.array([2], dtype=np.intp)
+        tm.assert_numpy_array_equal(res_cat, exp)
+        tm.assert_numpy_array_equal(res_ser, exp)
+
+        # Searching for single item array, side='left' (default)
+        res_cat = c1.searchsorted(['bread'])
+        res_ser = s1.searchsorted(['bread'])
+        exp = np.array([3], dtype=np.intp)
+        tm.assert_numpy_array_equal(res_cat, exp)
+        tm.assert_numpy_array_equal(res_ser, exp)
+
+        # Searching for several items array, side='right'
+        res_cat = c1.searchsorted(['apple', 'bread'], side='right')
+        res_ser = s1.searchsorted(['apple', 'bread'], side='right')
+        exp = np.array([3, 5], dtype=np.intp)
+        tm.assert_numpy_array_equal(res_cat, exp)
+        tm.assert_numpy_array_equal(res_ser, exp)
+
+        # Searching for a single value that is not from the Categorical
+        pytest.raises(ValueError, lambda: c1.searchsorted('cucumber'))
+        pytest.raises(ValueError, lambda: s1.searchsorted('cucumber'))
+
+        # Searching for multiple values one of each is not from the Categorical
+        pytest.raises(ValueError,
+                      lambda: c1.searchsorted(['bread', 'cucumber']))
+        pytest.raises(ValueError,
+                      lambda: s1.searchsorted(['bread', 'cucumber']))
+
+        # searchsorted call for unordered Categorical
+        pytest.raises(ValueError, lambda: c2.searchsorted('apple'))
+        pytest.raises(ValueError, lambda: s2.searchsorted('apple'))
+
+        with tm.assert_produces_warning(FutureWarning):
+            res = c1.searchsorted(v=['bread'])
+            exp = np.array([3], dtype=np.intp)
+            tm.assert_numpy_array_equal(res, exp)
+
+    def test_unique(self):
+        # categories are reordered based on value when ordered=False
+        cat = Categorical(["a", "b"])
+        exp = Index(["a", "b"])
+        res = cat.unique()
+        tm.assert_index_equal(res.categories, exp)
+        tm.assert_categorical_equal(res, cat)
+
+        cat = Categorical(["a", "b", "a", "a"], categories=["a", "b", "c"])
+        res = cat.unique()
+        tm.assert_index_equal(res.categories, exp)
+        tm.assert_categorical_equal(res, Categorical(exp))
+
+        cat = Categorical(["c", "a", "b", "a", "a"],
+                          categories=["a", "b", "c"])
+        exp = Index(["c", "a", "b"])
+        res = cat.unique()
+        tm.assert_index_equal(res.categories, exp)
+        exp_cat = Categorical(exp, categories=['c', 'a', 'b'])
+        tm.assert_categorical_equal(res, exp_cat)
+
+        # nan must be removed
+        cat = Categorical(["b", np.nan, "b", np.nan, "a"],
+                          categories=["a", "b", "c"])
+        res = cat.unique()
+        exp = Index(["b", "a"])
+        tm.assert_index_equal(res.categories, exp)
+        exp_cat = Categorical(["b", np.nan, "a"], categories=["b", "a"])
+        tm.assert_categorical_equal(res, exp_cat)
+
+    def test_unique_ordered(self):
+        # keep categories order when ordered=True
+        cat = Categorical(['b', 'a', 'b'], categories=['a', 'b'], ordered=True)
+        res = cat.unique()
+        exp_cat = Categorical(['b', 'a'], categories=['a', 'b'], ordered=True)
+        tm.assert_categorical_equal(res, exp_cat)
+
+        cat = Categorical(['c', 'b', 'a', 'a'], categories=['a', 'b', 'c'],
+                          ordered=True)
+        res = cat.unique()
+        exp_cat = Categorical(['c', 'b', 'a'], categories=['a', 'b', 'c'],
+                              ordered=True)
+        tm.assert_categorical_equal(res, exp_cat)
+
+        cat = Categorical(['b', 'a', 'a'], categories=['a', 'b', 'c'],
+                          ordered=True)
+        res = cat.unique()
+        exp_cat = Categorical(['b', 'a'], categories=['a', 'b'], ordered=True)
+        tm.assert_categorical_equal(res, exp_cat)
+
+        cat = Categorical(['b', 'b', np.nan, 'a'], categories=['a', 'b', 'c'],
+                          ordered=True)
+        res = cat.unique()
+        exp_cat = Categorical(['b', np.nan, 'a'], categories=['a', 'b'],
+                              ordered=True)
+        tm.assert_categorical_equal(res, exp_cat)
+
+    def test_unique_index_series(self):
+        c = Categorical([3, 1, 2, 2, 1], categories=[3, 2, 1])
+        # Categorical.unique sorts categories by appearance order
+        # if ordered=False
+        exp = Categorical([3, 1, 2], categories=[3, 1, 2])
+        tm.assert_categorical_equal(c.unique(), exp)
+
+        tm.assert_index_equal(Index(c).unique(), Index(exp))
+        tm.assert_categorical_equal(Series(c).unique(), exp)
+
+        c = Categorical([1, 1, 2, 2], categories=[3, 2, 1])
+        exp = Categorical([1, 2], categories=[1, 2])
+        tm.assert_categorical_equal(c.unique(), exp)
+        tm.assert_index_equal(Index(c).unique(), Index(exp))
+        tm.assert_categorical_equal(Series(c).unique(), exp)
+
+        c = Categorical([3, 1, 2, 2, 1], categories=[3, 2, 1], ordered=True)
+        # Categorical.unique keeps categories order if ordered=True
+        exp = Categorical([3, 1, 2], categories=[3, 2, 1], ordered=True)
+        tm.assert_categorical_equal(c.unique(), exp)
+
+        tm.assert_index_equal(Index(c).unique(), Index(exp))
+        tm.assert_categorical_equal(Series(c).unique(), exp)
+
+    def test_shift(self):
+        # GH 9416
+        cat = Categorical(['a', 'b', 'c', 'd', 'a'])
+
+        # shift forward
+        sp1 = cat.shift(1)
+        xp1 = Categorical([np.nan, 'a', 'b', 'c', 'd'])
+        tm.assert_categorical_equal(sp1, xp1)
+        tm.assert_categorical_equal(cat[:-1], sp1[1:])
+
+        # shift back
+        sn2 = cat.shift(-2)
+        xp2 = Categorical(['c', 'd', 'a', np.nan, np.nan],
+                          categories=['a', 'b', 'c', 'd'])
+        tm.assert_categorical_equal(sn2, xp2)
+        tm.assert_categorical_equal(cat[2:], sn2[:-2])
+
+        # shift by zero
+        tm.assert_categorical_equal(cat, cat.shift(0))
+
+    def test_nbytes(self):
+        cat = Categorical([1, 2, 3])
+        exp = 3 + 3 * 8  # 3 int8s for values + 3 int64s for categories
+        assert cat.nbytes == exp
+
+    def test_memory_usage(self):
+        cat = Categorical([1, 2, 3])
+
+        # .categories is an index, so we include the hashtable
+        assert 0 < cat.nbytes <= cat.memory_usage()
+        assert 0 < cat.nbytes <= cat.memory_usage(deep=True)
+
+        cat = Categorical(['foo', 'foo', 'bar'])
+        assert cat.memory_usage(deep=True) > cat.nbytes
+
+        if not PYPY:
+            # sys.getsizeof will call the .memory_usage with
+            # deep=True, and add on some GC overhead
+            diff = cat.memory_usage(deep=True) - sys.getsizeof(cat)
+            assert abs(diff) < 100
+
+    def test_map(self):
+        c = Categorical(list('ABABC'), categories=list('CBA'), ordered=True)
+        result = c.map(lambda x: x.lower())
+        exp = Categorical(list('ababc'), categories=list('cba'), ordered=True)
+        tm.assert_categorical_equal(result, exp)
+
+        c = Categorical(list('ABABC'), categories=list('ABC'), ordered=False)
+        result = c.map(lambda x: x.lower())
+        exp = Categorical(list('ababc'), categories=list('abc'), ordered=False)
+        tm.assert_categorical_equal(result, exp)
+
+        result = c.map(lambda x: 1)
+        # GH 12766: Return an index not an array
+        tm.assert_index_equal(result, Index(np.array([1] * 5, dtype=np.int64)))
+
+    def test_validate_inplace(self):
+        cat = Categorical(['A', 'B', 'B', 'C', 'A'])
+        invalid_values = [1, "True", [1, 2, 3], 5.0]
+
+        for value in invalid_values:
+            with pytest.raises(ValueError):
+                cat.set_ordered(value=True, inplace=value)
+
+            with pytest.raises(ValueError):
+                cat.as_ordered(inplace=value)
+
+            with pytest.raises(ValueError):
+                cat.as_unordered(inplace=value)
+
+            with pytest.raises(ValueError):
+                cat.set_categories(['X', 'Y', 'Z'], rename=True, inplace=value)
+
+            with pytest.raises(ValueError):
+                cat.rename_categories(['X', 'Y', 'Z'], inplace=value)
+
+            with pytest.raises(ValueError):
+                cat.reorder_categories(
+                    ['X', 'Y', 'Z'], ordered=True, inplace=value)
+
+            with pytest.raises(ValueError):
+                cat.add_categories(
+                    new_categories=['D', 'E', 'F'], inplace=value)
+
+            with pytest.raises(ValueError):
+                cat.remove_categories(removals=['D', 'E', 'F'], inplace=value)
+
+            with pytest.raises(ValueError):
+                cat.remove_unused_categories(inplace=value)
+
+            with pytest.raises(ValueError):
+                cat.sort_values(inplace=value)
+
+    def test_repeat(self):
+        # GH10183
+        cat = Categorical(["a", "b"], categories=["a", "b"])
+        exp = Categorical(["a", "a", "b", "b"], categories=["a", "b"])
+        res = cat.repeat(2)
+        tm.assert_categorical_equal(res, exp)
+
+    def test_numpy_repeat(self):
+        cat = Categorical(["a", "b"], categories=["a", "b"])
+        exp = Categorical(["a", "a", "b", "b"], categories=["a", "b"])
+        tm.assert_categorical_equal(np.repeat(cat, 2), exp)
+
+        msg = "the 'axis' parameter is not supported"
+        tm.assert_raises_regex(ValueError, msg, np.repeat, cat, 2, axis=1)
+
+    def test_isna(self):
+        exp = np.array([False, False, True])
+        c = Categorical(["a", "b", np.nan])
+        res = c.isna()
+
+        tm.assert_numpy_array_equal(res, exp)