291 lines
9.3 KiB
Python
291 lines
9.3 KiB
Python
import collections
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from datetime import timedelta
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import (
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DatetimeIndex,
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Index,
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Interval,
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IntervalIndex,
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Series,
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Timedelta,
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TimedeltaIndex,
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)
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import pandas._testing as tm
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from pandas.tests.base.common import allow_na_ops
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def test_value_counts(index_or_series_obj):
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obj = index_or_series_obj
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obj = np.repeat(obj, range(1, len(obj) + 1))
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result = obj.value_counts()
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counter = collections.Counter(obj)
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expected = Series(dict(counter.most_common()), dtype=np.int64, name=obj.name)
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expected.index = expected.index.astype(obj.dtype)
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if isinstance(obj, pd.MultiIndex):
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expected.index = Index(expected.index)
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if not isinstance(result.dtype, np.dtype):
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# i.e IntegerDtype
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expected = expected.astype("Int64")
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# TODO(GH#32514): Order of entries with the same count is inconsistent
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# on CI (gh-32449)
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if obj.duplicated().any():
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result = result.sort_index()
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expected = expected.sort_index()
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("null_obj", [np.nan, None])
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def test_value_counts_null(null_obj, index_or_series_obj):
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orig = index_or_series_obj
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obj = orig.copy()
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if not allow_na_ops(obj):
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pytest.skip("type doesn't allow for NA operations")
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elif len(obj) < 1:
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pytest.skip("Test doesn't make sense on empty data")
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elif isinstance(orig, pd.MultiIndex):
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pytest.skip(f"MultiIndex can't hold '{null_obj}'")
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values = obj._values
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values[0:2] = null_obj
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klass = type(obj)
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repeated_values = np.repeat(values, range(1, len(values) + 1))
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obj = klass(repeated_values, dtype=obj.dtype)
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# because np.nan == np.nan is False, but None == None is True
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# np.nan would be duplicated, whereas None wouldn't
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counter = collections.Counter(obj.dropna())
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expected = Series(dict(counter.most_common()), dtype=np.int64)
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expected.index = expected.index.astype(obj.dtype)
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result = obj.value_counts()
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if obj.duplicated().any():
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# TODO(GH#32514):
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# Order of entries with the same count is inconsistent on CI (gh-32449)
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expected = expected.sort_index()
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result = result.sort_index()
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if not isinstance(result.dtype, np.dtype):
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# i.e IntegerDtype
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expected = expected.astype("Int64")
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tm.assert_series_equal(result, expected)
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expected[null_obj] = 3
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result = obj.value_counts(dropna=False)
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if obj.duplicated().any():
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# TODO(GH#32514):
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# Order of entries with the same count is inconsistent on CI (gh-32449)
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expected = expected.sort_index()
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result = result.sort_index()
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tm.assert_series_equal(result, expected)
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def test_value_counts_inferred(index_or_series):
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klass = index_or_series
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s_values = ["a", "b", "b", "b", "b", "c", "d", "d", "a", "a"]
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s = klass(s_values)
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expected = Series([4, 3, 2, 1], index=["b", "a", "d", "c"])
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tm.assert_series_equal(s.value_counts(), expected)
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if isinstance(s, Index):
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exp = Index(np.unique(np.array(s_values, dtype=np.object_)))
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tm.assert_index_equal(s.unique(), exp)
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else:
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exp = np.unique(np.array(s_values, dtype=np.object_))
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tm.assert_numpy_array_equal(s.unique(), exp)
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assert s.nunique() == 4
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# don't sort, have to sort after the fact as not sorting is
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# platform-dep
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hist = s.value_counts(sort=False).sort_values()
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expected = Series([3, 1, 4, 2], index=list("acbd")).sort_values()
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tm.assert_series_equal(hist, expected)
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# sort ascending
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hist = s.value_counts(ascending=True)
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expected = Series([1, 2, 3, 4], index=list("cdab"))
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tm.assert_series_equal(hist, expected)
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# relative histogram.
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hist = s.value_counts(normalize=True)
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expected = Series([0.4, 0.3, 0.2, 0.1], index=["b", "a", "d", "c"])
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tm.assert_series_equal(hist, expected)
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def test_value_counts_bins(index_or_series):
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klass = index_or_series
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s_values = ["a", "b", "b", "b", "b", "c", "d", "d", "a", "a"]
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s = klass(s_values)
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# bins
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msg = "bins argument only works with numeric data"
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with pytest.raises(TypeError, match=msg):
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s.value_counts(bins=1)
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s1 = Series([1, 1, 2, 3])
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res1 = s1.value_counts(bins=1)
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exp1 = Series({Interval(0.997, 3.0): 4})
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tm.assert_series_equal(res1, exp1)
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res1n = s1.value_counts(bins=1, normalize=True)
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exp1n = Series({Interval(0.997, 3.0): 1.0})
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tm.assert_series_equal(res1n, exp1n)
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if isinstance(s1, Index):
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tm.assert_index_equal(s1.unique(), Index([1, 2, 3]))
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else:
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exp = np.array([1, 2, 3], dtype=np.int64)
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tm.assert_numpy_array_equal(s1.unique(), exp)
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assert s1.nunique() == 3
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# these return the same
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res4 = s1.value_counts(bins=4, dropna=True)
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intervals = IntervalIndex.from_breaks([0.997, 1.5, 2.0, 2.5, 3.0])
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exp4 = Series([2, 1, 1, 0], index=intervals.take([0, 1, 3, 2]))
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tm.assert_series_equal(res4, exp4)
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res4 = s1.value_counts(bins=4, dropna=False)
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intervals = IntervalIndex.from_breaks([0.997, 1.5, 2.0, 2.5, 3.0])
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exp4 = Series([2, 1, 1, 0], index=intervals.take([0, 1, 3, 2]))
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tm.assert_series_equal(res4, exp4)
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res4n = s1.value_counts(bins=4, normalize=True)
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exp4n = Series([0.5, 0.25, 0.25, 0], index=intervals.take([0, 1, 3, 2]))
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tm.assert_series_equal(res4n, exp4n)
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# handle NA's properly
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s_values = ["a", "b", "b", "b", np.nan, np.nan, "d", "d", "a", "a", "b"]
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s = klass(s_values)
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expected = Series([4, 3, 2], index=["b", "a", "d"])
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tm.assert_series_equal(s.value_counts(), expected)
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if isinstance(s, Index):
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exp = Index(["a", "b", np.nan, "d"])
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tm.assert_index_equal(s.unique(), exp)
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else:
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exp = np.array(["a", "b", np.nan, "d"], dtype=object)
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tm.assert_numpy_array_equal(s.unique(), exp)
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assert s.nunique() == 3
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s = klass({}) if klass is dict else klass({}, dtype=object)
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expected = Series([], dtype=np.int64)
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tm.assert_series_equal(s.value_counts(), expected, check_index_type=False)
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# returned dtype differs depending on original
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if isinstance(s, Index):
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tm.assert_index_equal(s.unique(), Index([]), exact=False)
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else:
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tm.assert_numpy_array_equal(s.unique(), np.array([]), check_dtype=False)
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assert s.nunique() == 0
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def test_value_counts_datetime64(index_or_series):
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klass = index_or_series
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# GH 3002, datetime64[ns]
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# don't test names though
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df = pd.DataFrame(
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{
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"person_id": ["xxyyzz", "xxyyzz", "xxyyzz", "xxyyww", "foofoo", "foofoo"],
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"dt": pd.to_datetime(
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[
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"2010-01-01",
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"2010-01-01",
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"2010-01-01",
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"2009-01-01",
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"2008-09-09",
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"2008-09-09",
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]
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),
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"food": ["PIE", "GUM", "EGG", "EGG", "PIE", "GUM"],
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}
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)
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s = klass(df["dt"].copy())
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s.name = None
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idx = pd.to_datetime(
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["2010-01-01 00:00:00", "2008-09-09 00:00:00", "2009-01-01 00:00:00"]
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)
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expected_s = Series([3, 2, 1], index=idx)
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tm.assert_series_equal(s.value_counts(), expected_s)
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expected = np.array(
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["2010-01-01 00:00:00", "2009-01-01 00:00:00", "2008-09-09 00:00:00"],
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dtype="datetime64[ns]",
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)
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if isinstance(s, Index):
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tm.assert_index_equal(s.unique(), DatetimeIndex(expected))
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else:
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tm.assert_numpy_array_equal(s.unique(), expected)
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assert s.nunique() == 3
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# with NaT
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s = df["dt"].copy()
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s = klass(list(s.values) + [pd.NaT] * 4)
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result = s.value_counts()
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assert result.index.dtype == "datetime64[ns]"
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tm.assert_series_equal(result, expected_s)
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result = s.value_counts(dropna=False)
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expected_s = pd.concat([Series([4], index=DatetimeIndex([pd.NaT])), expected_s])
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tm.assert_series_equal(result, expected_s)
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assert s.dtype == "datetime64[ns]"
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unique = s.unique()
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assert unique.dtype == "datetime64[ns]"
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# numpy_array_equal cannot compare pd.NaT
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if isinstance(s, Index):
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exp_idx = DatetimeIndex(expected.tolist() + [pd.NaT])
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tm.assert_index_equal(unique, exp_idx)
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else:
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tm.assert_numpy_array_equal(unique[:3], expected)
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assert pd.isna(unique[3])
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assert s.nunique() == 3
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assert s.nunique(dropna=False) == 4
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# timedelta64[ns]
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td = df.dt - df.dt + timedelta(1)
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td = klass(td, name="dt")
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result = td.value_counts()
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expected_s = Series([6], index=[Timedelta("1day")], name="dt")
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tm.assert_series_equal(result, expected_s)
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expected = TimedeltaIndex(["1 days"], name="dt")
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if isinstance(td, Index):
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tm.assert_index_equal(td.unique(), expected)
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else:
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tm.assert_numpy_array_equal(td.unique(), expected.values)
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td2 = timedelta(1) + (df.dt - df.dt)
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td2 = klass(td2, name="dt")
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result2 = td2.value_counts()
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tm.assert_series_equal(result2, expected_s)
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@pytest.mark.parametrize("dropna", [True, False])
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def test_value_counts_with_nan(dropna, index_or_series):
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# GH31944
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klass = index_or_series
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values = [True, pd.NA, np.nan]
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obj = klass(values)
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res = obj.value_counts(dropna=dropna)
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if dropna is True:
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expected = Series([1], index=[True])
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else:
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expected = Series([1, 1, 1], index=[True, pd.NA, np.nan])
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tm.assert_series_equal(res, expected)
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