206 lines
7.9 KiB
Python
206 lines
7.9 KiB
Python
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 Categorical, CategoricalIndex, Series
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import pandas._testing as tm
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class TestSeriesValueCounts:
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def test_value_counts_datetime(self):
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# most dtypes are tested in tests/base
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values = [
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pd.Timestamp("2011-01-01 09:00"),
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pd.Timestamp("2011-01-01 10:00"),
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pd.Timestamp("2011-01-01 11:00"),
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pd.Timestamp("2011-01-01 09:00"),
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pd.Timestamp("2011-01-01 09:00"),
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pd.Timestamp("2011-01-01 11:00"),
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]
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exp_idx = pd.DatetimeIndex(
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["2011-01-01 09:00", "2011-01-01 11:00", "2011-01-01 10:00"]
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)
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exp = Series([3, 2, 1], index=exp_idx, name="xxx")
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ser = Series(values, name="xxx")
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tm.assert_series_equal(ser.value_counts(), exp)
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# check DatetimeIndex outputs the same result
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idx = pd.DatetimeIndex(values, name="xxx")
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tm.assert_series_equal(idx.value_counts(), exp)
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# normalize
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exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="xxx")
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tm.assert_series_equal(ser.value_counts(normalize=True), exp)
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tm.assert_series_equal(idx.value_counts(normalize=True), exp)
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def test_value_counts_datetime_tz(self):
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values = [
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pd.Timestamp("2011-01-01 09:00", tz="US/Eastern"),
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pd.Timestamp("2011-01-01 10:00", tz="US/Eastern"),
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pd.Timestamp("2011-01-01 11:00", tz="US/Eastern"),
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pd.Timestamp("2011-01-01 09:00", tz="US/Eastern"),
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pd.Timestamp("2011-01-01 09:00", tz="US/Eastern"),
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pd.Timestamp("2011-01-01 11:00", tz="US/Eastern"),
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]
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exp_idx = pd.DatetimeIndex(
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["2011-01-01 09:00", "2011-01-01 11:00", "2011-01-01 10:00"],
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tz="US/Eastern",
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)
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exp = Series([3, 2, 1], index=exp_idx, name="xxx")
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ser = Series(values, name="xxx")
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tm.assert_series_equal(ser.value_counts(), exp)
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idx = pd.DatetimeIndex(values, name="xxx")
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tm.assert_series_equal(idx.value_counts(), exp)
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exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="xxx")
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tm.assert_series_equal(ser.value_counts(normalize=True), exp)
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tm.assert_series_equal(idx.value_counts(normalize=True), exp)
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def test_value_counts_period(self):
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values = [
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pd.Period("2011-01", freq="M"),
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pd.Period("2011-02", freq="M"),
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pd.Period("2011-03", freq="M"),
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pd.Period("2011-01", freq="M"),
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pd.Period("2011-01", freq="M"),
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pd.Period("2011-03", freq="M"),
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]
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exp_idx = pd.PeriodIndex(["2011-01", "2011-03", "2011-02"], freq="M")
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exp = Series([3, 2, 1], index=exp_idx, name="xxx")
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ser = Series(values, name="xxx")
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tm.assert_series_equal(ser.value_counts(), exp)
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# check DatetimeIndex outputs the same result
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idx = pd.PeriodIndex(values, name="xxx")
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tm.assert_series_equal(idx.value_counts(), exp)
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# normalize
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exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="xxx")
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tm.assert_series_equal(ser.value_counts(normalize=True), exp)
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tm.assert_series_equal(idx.value_counts(normalize=True), exp)
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def test_value_counts_categorical_ordered(self):
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# most dtypes are tested in tests/base
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values = Categorical([1, 2, 3, 1, 1, 3], ordered=True)
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exp_idx = CategoricalIndex([1, 3, 2], categories=[1, 2, 3], ordered=True)
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exp = Series([3, 2, 1], index=exp_idx, name="xxx")
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ser = Series(values, name="xxx")
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tm.assert_series_equal(ser.value_counts(), exp)
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# check CategoricalIndex outputs the same result
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idx = CategoricalIndex(values, name="xxx")
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tm.assert_series_equal(idx.value_counts(), exp)
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# normalize
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exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="xxx")
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tm.assert_series_equal(ser.value_counts(normalize=True), exp)
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tm.assert_series_equal(idx.value_counts(normalize=True), exp)
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def test_value_counts_categorical_not_ordered(self):
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values = Categorical([1, 2, 3, 1, 1, 3], ordered=False)
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exp_idx = CategoricalIndex([1, 3, 2], categories=[1, 2, 3], ordered=False)
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exp = Series([3, 2, 1], index=exp_idx, name="xxx")
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ser = Series(values, name="xxx")
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tm.assert_series_equal(ser.value_counts(), exp)
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# check CategoricalIndex outputs the same result
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idx = CategoricalIndex(values, name="xxx")
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tm.assert_series_equal(idx.value_counts(), exp)
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# normalize
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exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="xxx")
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tm.assert_series_equal(ser.value_counts(normalize=True), exp)
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tm.assert_series_equal(idx.value_counts(normalize=True), exp)
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def test_value_counts_categorical(self):
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# GH#12835
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cats = Categorical(list("abcccb"), categories=list("cabd"))
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ser = Series(cats, name="xxx")
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res = ser.value_counts(sort=False)
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exp_index = CategoricalIndex(list("cabd"), categories=cats.categories)
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exp = Series([3, 1, 2, 0], name="xxx", index=exp_index)
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tm.assert_series_equal(res, exp)
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res = ser.value_counts(sort=True)
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exp_index = CategoricalIndex(list("cbad"), categories=cats.categories)
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exp = Series([3, 2, 1, 0], name="xxx", index=exp_index)
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tm.assert_series_equal(res, exp)
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# check object dtype handles the Series.name as the same
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# (tested in tests/base)
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ser = Series(["a", "b", "c", "c", "c", "b"], name="xxx")
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res = ser.value_counts()
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exp = Series([3, 2, 1], name="xxx", index=["c", "b", "a"])
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tm.assert_series_equal(res, exp)
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def test_value_counts_categorical_with_nan(self):
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# see GH#9443
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# sanity check
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ser = Series(["a", "b", "a"], dtype="category")
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exp = Series([2, 1], index=CategoricalIndex(["a", "b"]))
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res = ser.value_counts(dropna=True)
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tm.assert_series_equal(res, exp)
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res = ser.value_counts(dropna=True)
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tm.assert_series_equal(res, exp)
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# same Series via two different constructions --> same behaviour
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series = [
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Series(["a", "b", None, "a", None, None], dtype="category"),
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Series(
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Categorical(["a", "b", None, "a", None, None], categories=["a", "b"])
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),
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]
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for ser in series:
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# None is a NaN value, so we exclude its count here
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exp = Series([2, 1], index=CategoricalIndex(["a", "b"]))
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res = ser.value_counts(dropna=True)
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tm.assert_series_equal(res, exp)
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# we don't exclude the count of None and sort by counts
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exp = Series([3, 2, 1], index=CategoricalIndex([np.nan, "a", "b"]))
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res = ser.value_counts(dropna=False)
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tm.assert_series_equal(res, exp)
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# When we aren't sorting by counts, and np.nan isn't a
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# category, it should be last.
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exp = Series([2, 1, 3], index=CategoricalIndex(["a", "b", np.nan]))
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res = ser.value_counts(dropna=False, sort=False)
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tm.assert_series_equal(res, exp)
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@pytest.mark.parametrize(
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"ser, dropna, exp",
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[
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(
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Series([False, True, True, pd.NA]),
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False,
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Series([2, 1, 1], index=[True, pd.NA, False]),
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),
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(
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Series([False, True, True, pd.NA]),
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True,
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Series([2, 1], index=[True, False]),
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),
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(
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Series(range(3), index=[True, False, np.nan]).index,
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False,
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Series([1, 1, 1], index=[pd.NA, False, True]),
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),
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],
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)
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def test_value_counts_bool_with_nan(self, ser, dropna, exp):
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# GH32146
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out = ser.value_counts(dropna=dropna)
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tm.assert_series_equal(out, exp)
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