text: from functools import partial
  
  import numpy as np
  import pytest
  
  from pandas import (
      DataFrame,
      Series,
      concat,
      isna,
      notna,
  )
  import pandas._testing as tm
  
  import pandas.tseries.offsets as offsets
  
  
  def scoreatpercentile(a, per):
      values = np.sort(a, axis=0)
  
      idx = int(per / 1.0 * (values.shape[0] - 1))
  
      if idx == values.shape[0] - 1:
          retval = values[-1]
  
      else:
          qlow = idx / (values.shape[0] - 1)
          qhig = (idx + 1) / (values.shape[0] - 1)
          vlow = values[idx]
          vhig = values[idx + 1]
          retval = vlow + (vhig - vlow) * (per - qlow) / (qhig - qlow)
  
      return retval
  
  
  @pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0])
  def test_series(series, q):
      compare_func = partial(scoreatpercentile, per=q)
      result = series.rolling(50).quantile(q)
      assert isinstance(result, Series)
      tm.assert_almost_equal(result.iloc[-1], compare_func(series[-50:]))
  
  
  @pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0])
  def test_frame(raw, frame, q):
      compare_func = partial(scoreatpercentile, per=q)
      result = frame.rolling(50).quantile(q)
      assert isinstance(result, DataFrame)
      tm.assert_series_equal(
          result.iloc[-1, :],
          frame.iloc[-50:, :].apply(compare_func, axis=0, raw=raw),
          check_names=False,
      )
  
  
  @pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0])
  def test_time_rule_series(series, q):
      compare_func = partial(scoreatpercentile, per=q)
      win = 25
      ser = series[::2].resample("B").mean()
      series_result = ser.rolling(window=win, min_periods=10).quantile(q)
      last_date = series_result.index[-1]
      prev_date = last_date - 24 * offsets.BDay()
  
      trunc_series = series[::2].truncate(prev_date, last_date)
      tm.assert_almost_equal(series_result[-1], compare_func(trunc_series))
  
  
  @pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0])
  def test_time_rule_frame(raw, frame, q):
      compare_func = partial(scoreatpercentile, per=q)
      win = 25
      frm = frame[::2].resample("B").mean()
      frame_result = frm.rolling(window=win, min_periods=10).quantile(q)
      last_date = frame_result.index[-1]
      prev_date = last_date - 24 * offsets.BDay()
  
      trunc_frame = frame[::2].truncate(prev_date, last_date)
      tm.assert_series_equal(
          frame_result.xs(last_date),
          trunc_frame.apply(compare_func, raw=raw),
          check_names=False,
      )
  
  
  @pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0])
  def test_nans(q):
      compare_func = partial(scoreatpercentile, per=q)
      obj = Series(np.random.randn(50))
      obj[:10] = np.NaN
      obj[-10:] = np.NaN
  
      result = obj.rolling(50, min_periods=30).quantile(q)
      tm.assert_almost_equal(result.iloc[-1], compare_func(obj[10:-10]))
  
      # min_periods is working correctly
      result = obj.rolling(20, min_periods=15).quantile(q)
      assert isna(result.iloc[23])
      assert not isna(result.iloc[24])
  
      assert not isna(result.iloc[-6])
      assert isna(result.iloc[-5])
  
      obj2 = Series(np.random.randn(20))
      result = obj2.rolling(10, min_periods=5).quantile(q)
      assert isna(result.iloc[3])
      assert notna(result.iloc[4])
  
      result0 = obj.rolling(20, min_periods=0).quantile(q)
      result1 = obj.rolling(20, min_periods=1).quantile(q)
      tm.assert_almost_equal(result0, result1)
  
  
  @pytest.mark.parametrize("minp", [0, 99, 100])
  @pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0])
  def test_min_periods(series, minp, q):
      result = series.rolling(len(series) + 1, min_periods=minp).quantile(q)
      expected = series.rolling(len(series), min_periods=minp).quantile(q)
      nan_mask = isna(result)
      tm.assert_series_equal(nan_mask, isna(expected))
  
      nan_mask = ~nan_mask
      tm.assert_almost_equal(result[nan_mask], expected[nan_mask])
  
  
  @pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0])
  def test_center(q):
      obj = Series(np.random.randn(50))
      obj[:10] = np.NaN
      obj[-10:] = np.NaN
  
      result = obj.rolling(20, center=True).quantile(q)
      expected = (
          concat([obj, Series([np.NaN] * 9)])
          .rolling(20)
          .quantile(q)[9:]
          .reset_index(drop=True)
      )
      tm.assert_series_equal(result, expected)
  
  
  @pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0])
  def test_center_reindex_series(series, q):
      # shifter index
      s = [f"x{x:d}" for x in range(12)]
  
      series_xp = (
          series.reindex(list(series.index) + s)
          .rolling(window=25)
          .quantile(q)
          .shift(-12)
          .reindex(series.index)
      )
  
      series_rs = series.rolling(window=25, center=True).quantile(q)
      tm.assert_series_equal(series_xp, series_rs)
  
  
  @pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0])
  def test_center_reindex_frame(frame, q):
      # shifter index
      s = [f"x{x:d}" for x in range(12)]
  
      frame_xp = (
          frame.reindex(list(frame.index) + s)
          .rolling(window=25)
          .quantile(q)
          .shift(-12)
          .reindex(frame.index)
      )
      frame_rs = frame.rolling(window=25, center=True).quantile(q)
      tm.assert_frame_equal(frame_xp, frame_rs)
  