text: from __future__ import annotations
  
  import collections
  from datetime import datetime
  from decimal import Decimal
  from functools import wraps
  import operator
  import os
  import re
  import string
  from typing import (
      TYPE_CHECKING,
      Callable,
      ContextManager,
      Counter,
      Iterable,
  )
  import warnings
  
  import numpy as np
  
  from pandas._config.localization import (  # noqa:F401
      can_set_locale,
      get_locales,
      set_locale,
  )
  
  from pandas._typing import Dtype
  
  from pandas.core.dtypes.common import (
      is_float_dtype,
      is_integer_dtype,
      is_sequence,
      is_unsigned_integer_dtype,
      pandas_dtype,
  )
  
  import pandas as pd
  from pandas import (
      Categorical,
      CategoricalIndex,
      DataFrame,
      DatetimeIndex,
      Index,
      IntervalIndex,
      MultiIndex,
      NumericIndex,
      RangeIndex,
      Series,
      bdate_range,
  )
  from pandas._testing._io import (  # noqa:F401
      close,
      network,
      round_trip_localpath,
      round_trip_pathlib,
      round_trip_pickle,
      with_connectivity_check,
      write_to_compressed,
  )
  from pandas._testing._random import (  # noqa:F401
      randbool,
      rands,
      rands_array,
      randu_array,
  )
  from pandas._testing._warnings import assert_produces_warning  # noqa:F401
  from pandas._testing.asserters import (  # noqa:F401
      assert_almost_equal,
      assert_attr_equal,
      assert_categorical_equal,
      assert_class_equal,
      assert_contains_all,
      assert_copy,
      assert_datetime_array_equal,
      assert_dict_equal,
      assert_equal,
      assert_extension_array_equal,
      assert_frame_equal,
      assert_index_equal,
      assert_indexing_slices_equivalent,
      assert_interval_array_equal,
      assert_is_sorted,
      assert_is_valid_plot_return_object,
      assert_numpy_array_equal,
      assert_period_array_equal,
      assert_series_equal,
      assert_sp_array_equal,
      assert_timedelta_array_equal,
      raise_assert_detail,
  )
  from pandas._testing.compat import (  # noqa:F401
      get_dtype,
      get_obj,
  )
  from pandas._testing.contexts import (  # noqa:F401
      RNGContext,
      decompress_file,
      ensure_clean,
      ensure_clean_dir,
      ensure_safe_environment_variables,
      set_timezone,
      use_numexpr,
      with_csv_dialect,
  )
  from pandas.core.api import (
      Float64Index,
      Int64Index,
      UInt64Index,
  )
  from pandas.core.arrays import (
      BaseMaskedArray,
      ExtensionArray,
      PandasArray,
  )
  from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
  from pandas.core.construction import extract_array
  
  if TYPE_CHECKING:
      from pandas import (
          PeriodIndex,
          TimedeltaIndex,
      )
  
  _N = 30
  _K = 4
  
  UNSIGNED_INT_NUMPY_DTYPES: list[Dtype] = ["uint8", "uint16", "uint32", "uint64"]
  UNSIGNED_INT_EA_DTYPES: list[Dtype] = ["UInt8", "UInt16", "UInt32", "UInt64"]
  SIGNED_INT_NUMPY_DTYPES: list[Dtype] = [int, "int8", "int16", "int32", "int64"]
  SIGNED_INT_EA_DTYPES: list[Dtype] = ["Int8", "Int16", "Int32", "Int64"]
  ALL_INT_NUMPY_DTYPES = UNSIGNED_INT_NUMPY_DTYPES + SIGNED_INT_NUMPY_DTYPES
  ALL_INT_EA_DTYPES = UNSIGNED_INT_EA_DTYPES + SIGNED_INT_EA_DTYPES
  
  FLOAT_NUMPY_DTYPES: list[Dtype] = [float, "float32", "float64"]
  FLOAT_EA_DTYPES: list[Dtype] = ["Float32", "Float64"]
  COMPLEX_DTYPES: list[Dtype] = [complex, "complex64", "complex128"]
  STRING_DTYPES: list[Dtype] = [str, "str", "U"]
  
  DATETIME64_DTYPES: list[Dtype] = ["datetime64[ns]", "M8[ns]"]
  TIMEDELTA64_DTYPES: list[Dtype] = ["timedelta64[ns]", "m8[ns]"]
  
  BOOL_DTYPES: list[Dtype] = [bool, "bool"]
  BYTES_DTYPES: list[Dtype] = [bytes, "bytes"]
  OBJECT_DTYPES: list[Dtype] = [object, "object"]
  
  ALL_REAL_NUMPY_DTYPES = FLOAT_NUMPY_DTYPES + ALL_INT_NUMPY_DTYPES
  ALL_NUMPY_DTYPES = (
      ALL_REAL_NUMPY_DTYPES
      + COMPLEX_DTYPES
      + STRING_DTYPES
      + DATETIME64_DTYPES
      + TIMEDELTA64_DTYPES
      + BOOL_DTYPES
      + OBJECT_DTYPES
      + BYTES_DTYPES
  )
  
  NARROW_NP_DTYPES = [
      np.float16,
      np.float32,
      np.int8,
      np.int16,
      np.int32,
      np.uint8,
      np.uint16,
      np.uint32,
  ]
  
  NULL_OBJECTS = [None, np.nan, pd.NaT, float("nan"), pd.NA, Decimal("NaN")]
  NP_NAT_OBJECTS = [
      cls("NaT", unit)
      for cls in [np.datetime64, np.timedelta64]
      for unit in [
          "Y",
          "M",
          "W",
          "D",
          "h",
          "m",
          "s",
          "ms",
          "us",
          "ns",
          "ps",
          "fs",
          "as",
      ]
  ]
  
  EMPTY_STRING_PATTERN = re.compile("^$")
  
  # set testing_mode
  _testing_mode_warnings = (DeprecationWarning, ResourceWarning)
  
  
  def set_testing_mode():
      # set the testing mode filters
      testing_mode = os.environ.get("PANDAS_TESTING_MODE", "None")
      if "deprecate" in testing_mode:
          for category in _testing_mode_warnings:
              warnings.simplefilter("always", category)
  
  
  def reset_testing_mode():
      # reset the testing mode filters
      testing_mode = os.environ.get("PANDAS_TESTING_MODE", "None")
      if "deprecate" in testing_mode:
          for category in _testing_mode_warnings:
              warnings.simplefilter("ignore", category)
  
  
  set_testing_mode()
  
  
  def reset_display_options():
      """
      Reset the display options for printing and representing objects.
      """
      pd.reset_option("^display.", silent=True)
  
  
  # -----------------------------------------------------------------------------
  # Comparators
  
  
  def equalContents(arr1, arr2) -> bool:
      """
      Checks if the set of unique elements of arr1 and arr2 are equivalent.
      """
      return frozenset(arr1) == frozenset(arr2)
  
  
  def box_expected(expected, box_cls, transpose=True):
      """
      Helper function to wrap the expected output of a test in a given box_class.
  
      Parameters
      ----------
      expected : np.ndarray, Index, Series
      box_cls : {Index, Series, DataFrame}
  
      Returns
      -------
      subclass of box_cls
      """
      if box_cls is pd.array:
          if isinstance(expected, RangeIndex):
              # pd.array would return an IntegerArray
              expected = PandasArray(np.asarray(expected._values))
          else:
              expected = pd.array(expected)
      elif box_cls is Index:
          expected = Index._with_infer(expected)
      elif box_cls is Series:
          expected = Series(expected)
      elif box_cls is DataFrame:
          expected = Series(expected).to_frame()
          if transpose:
              # for vector operations, we need a DataFrame to be a single-row,
              #  not a single-column, in order to operate against non-DataFrame
              #  vectors of the same length. But convert to two rows to avoid
              #  single-row special cases in datetime arithmetic
              expected = expected.T
              expected = pd.concat([expected] * 2, ignore_index=True)
      elif box_cls is np.ndarray or box_cls is np.array:
          expected = np.array(expected)
      elif box_cls is to_array:
          expected = to_array(expected)
      else:
          raise NotImplementedError(box_cls)
      return expected
  
  
  def to_array(obj):
      """
      Similar to pd.array, but does not cast numpy dtypes to nullable dtypes.
      """
      # temporary implementation until we get pd.array in place
      dtype = getattr(obj, "dtype", None)
  
      if dtype is None:
          return np.asarray(obj)
  
      return extract_array(obj, extract_numpy=True)
  
  
  # -----------------------------------------------------------------------------
  # Others
  
  
  def getCols(k):
      return string.ascii_uppercase[:k]
  
  
  # make index
  def makeStringIndex(k=10, name=None):
      return Index(rands_array(nchars=10, size=k), name=name)
  
  
  def makeUnicodeIndex(k=10, name=None):
      return Index(randu_array(nchars=10, size=k), name=name)
  
  
  def makeCategoricalIndex(k=10, n=3, name=None, **kwargs):
      """make a length k index or n categories"""
      x = rands_array(nchars=4, size=n)
      return CategoricalIndex(
          Categorical.from_codes(np.arange(k) % n, categories=x), name=name, **kwargs
      )
  
  
  def makeIntervalIndex(k=10, name=None, **kwargs):
      """make a length k IntervalIndex"""
      x = np.linspace(0, 100, num=(k + 1))
      return IntervalIndex.from_breaks(x, name=name, **kwargs)
  
  
  def makeBoolIndex(k=10, name=None):
      if k == 1:
          return Index([True], name=name)
      elif k == 2:
          return Index([False, True], name=name)
      return Index([False, True] + [False] * (k - 2), name=name)
  
  
  def makeNumericIndex(k=10, name=None, *, dtype):
      dtype = pandas_dtype(dtype)
      assert isinstance(dtype, np.dtype)
  
      if is_integer_dtype(dtype):
          values = np.arange(k, dtype=dtype)
          if is_unsigned_integer_dtype(dtype):
              values += 2 ** (dtype.itemsize * 8 - 1)
      elif is_float_dtype(dtype):
          values = np.random.random_sample(k) - np.random.random_sample(1)
          values.sort()
          values = values * (10 ** np.random.randint(0, 9))
      else:
          raise NotImplementedError(f"wrong dtype {dtype}")
  
      return NumericIndex(values, dtype=dtype, name=name)
  
  
  def makeIntIndex(k=10, name=None):
      base_idx = makeNumericIndex(k, name=name, dtype="int64")
      return Int64Index(base_idx)
  
  
  def makeUIntIndex(k=10, name=None):
      base_idx = makeNumericIndex(k, name=name, dtype="uint64")
      return UInt64Index(base_idx)
  
  
  def makeRangeIndex(k=10, name=None, **kwargs):
      return RangeIndex(0, k, 1, name=name, **kwargs)
  
  
  def makeFloatIndex(k=10, name=None):
      base_idx = makeNumericIndex(k, name=name, dtype="float64")
      return Float64Index(base_idx)
  
  
  def makeDateIndex(k: int = 10, freq="B", name=None, **kwargs) -> DatetimeIndex:
      dt = datetime(2000, 1, 1)
      dr = bdate_range(dt, periods=k, freq=freq, name=name)
      return DatetimeIndex(dr, name=name, **kwargs)
  
  
  def makeTimedeltaIndex(k: int = 10, freq="D", name=None, **kwargs) -> TimedeltaIndex:
      return pd.timedelta_range(start="1 day", periods=k, freq=freq, name=name, **kwargs)
  
  
  def makePeriodIndex(k: int = 10, name=None, **kwargs) -> PeriodIndex:
      dt = datetime(2000, 1, 1)
      return pd.period_range(start=dt, periods=k, freq="B", name=name, **kwargs)
  
  
  def makeMultiIndex(k=10, names=None, **kwargs):
      return MultiIndex.from_product((("foo", "bar"), (1, 2)), names=names, **kwargs)
  
  
  _names = [
      "Alice",
      "Bob",
      "Charlie",
      "Dan",
      "Edith",
      "Frank",
      "George",
      "Hannah",
      "Ingrid",
      "Jerry",
      "Kevin",
      "Laura",
      "Michael",
      "Norbert",
      "Oliver",
      "Patricia",
      "Quinn",
      "Ray",
      "Sarah",
      "Tim",
      "Ursula",
      "Victor",
      "Wendy",
      "Xavier",
      "Yvonne",
      "Zelda",
  ]
  
  
  def _make_timeseries(start="2000-01-01", end="2000-12-31", freq="1D", seed=None):
      """
      Make a DataFrame with a DatetimeIndex
  
      Parameters
      ----------
      start : str or Timestamp, default "2000-01-01"
          The start of the index. Passed to date_range with `freq`.
      end : str or Timestamp, default "2000-12-31"
          The end of the index. Passed to date_range with `freq`.
      freq : str or Freq
          The frequency to use for the DatetimeIndex
      seed : int, optional
          The random state seed.
  
          * name : object dtype with string names
          * id : int dtype with
          * x, y : float dtype
  
      Examples
      --------
      >>> _make_timeseries()  # doctest: +SKIP
                    id    name         x         y
      timestamp
      2000-01-01   982   Frank  0.031261  0.986727
      2000-01-02  1025   Edith -0.086358 -0.032920
      2000-01-03   982   Edith  0.473177  0.298654
      2000-01-04  1009   Sarah  0.534344 -0.750377
      2000-01-05   963   Zelda -0.271573  0.054424
      ...          ...     ...       ...       ...
      2000-12-27   980  Ingrid -0.132333 -0.422195
      2000-12-28   972   Frank -0.376007 -0.298687
      2000-12-29  1009  Ursula -0.865047 -0.503133
      2000-12-30  1000  Hannah -0.063757 -0.507336
      2000-12-31   972     Tim -0.869120  0.531685
      """
      index = pd.date_range(start=start, end=end, freq=freq, name="timestamp")
      n = len(index)
      state = np.random.RandomState(seed)
      columns = {
          "name": state.choice(_names, size=n),
          "id": state.poisson(1000, size=n),
          "x": state.rand(n) * 2 - 1,
          "y": state.rand(n) * 2 - 1,
      }
      df = DataFrame(columns, index=index, columns=sorted(columns))
      if df.index[-1] == end:
          df = df.iloc[:-1]
      return df
  
  
  def index_subclass_makers_generator():
      make_index_funcs = [
          makeDateIndex,
          makePeriodIndex,
          makeTimedeltaIndex,
          makeRangeIndex,
          makeIntervalIndex,
          makeCategoricalIndex,
          makeMultiIndex,
      ]
      yield from make_index_funcs
  
  
  def all_timeseries_index_generator(k: int = 10) -> Iterable[Index]:
      """
      Generator which can be iterated over to get instances of all the classes
      which represent time-series.
  
      Parameters
      ----------
      k: length of each of the index instances
      """
      make_index_funcs: list[Callable[..., Index]] = [
          makeDateIndex,
          makePeriodIndex,
          makeTimedeltaIndex,
      ]
      for make_index_func in make_index_funcs:
          yield make_index_func(k=k)
  
  
  # make series
  def makeFloatSeries(name=None):
      index = makeStringIndex(_N)
      return Series(np.random.randn(_N), index=index, name=name)
  
  
  def makeStringSeries(name=None):
      index = makeStringIndex(_N)
      return Series(np.random.randn(_N), index=index, name=name)
  
  
  def makeObjectSeries(name=None):
      data = makeStringIndex(_N)
      data = Index(data, dtype=object)
      index = makeStringIndex(_N)
      return Series(data, index=index, name=name)
  
  
  def getSeriesData():
      index = makeStringIndex(_N)
      return {c: Series(np.random.randn(_N), index=index) for c in getCols(_K)}
  
  
  def makeTimeSeries(nper=None, freq="B", name=None):
      if nper is None:
          nper = _N
      return Series(
          np.random.randn(nper), index=makeDateIndex(nper, freq=freq), name=name
      )
  
  
  def makePeriodSeries(nper=None, name=None):
      if nper is None:
          nper = _N
      return Series(np.random.randn(nper), index=makePeriodIndex(nper), name=name)
  
  
  def getTimeSeriesData(nper=None, freq="B"):
      return {c: makeTimeSeries(nper, freq) for c in getCols(_K)}
  
  
  def getPeriodData(nper=None):
      return {c: makePeriodSeries(nper) for c in getCols(_K)}
  
  
  # make frame
  def makeTimeDataFrame(nper=None, freq="B"):
      data = getTimeSeriesData(nper, freq)
      return DataFrame(data)
  
  
  def makeDataFrame() -> DataFrame:
      data = getSeriesData()
      return DataFrame(data)
  
  
  def getMixedTypeDict():
      index = Index(["a", "b", "c", "d", "e"])
  
      data = {
          "A": [0.0, 1.0, 2.0, 3.0, 4.0],
          "B": [0.0, 1.0, 0.0, 1.0, 0.0],
          "C": ["foo1", "foo2", "foo3", "foo4", "foo5"],
          "D": bdate_range("1/1/2009", periods=5),
      }
  
      return index, data
  
  
  def makeMixedDataFrame():
      return DataFrame(getMixedTypeDict()[1])
  
  
  def makePeriodFrame(nper=None):
      data = getPeriodData(nper)
      return DataFrame(data)
  
  
  def makeCustomIndex(
      nentries, nlevels, prefix="#", names=False, ndupe_l=None, idx_type=None
  ):
      """
      Create an index/multindex with given dimensions, levels, names, etc'
  
      nentries - number of entries in index
      nlevels - number of levels (> 1 produces multindex)
      prefix - a string prefix for labels
      names - (Optional), bool or list of strings. if True will use default
         names, if false will use no names, if a list is given, the name of
         each level in the index will be taken from the list.
      ndupe_l - (Optional), list of ints, the number of rows for which the
         label will repeated at the corresponding level, you can specify just
         the first few, the rest will use the default ndupe_l of 1.
         len(ndupe_l) <= nlevels.
      idx_type - "i"/"f"/"s"/"u"/"dt"/"p"/"td".
         If idx_type is not None, `idx_nlevels` must be 1.
         "i"/"f" creates an integer/float index,
         "s"/"u" creates a string/unicode index
         "dt" create a datetime index.
         "td" create a datetime index.
  
          if unspecified, string labels will be generated.
      """
      if ndupe_l is None:
          ndupe_l = [1] * nlevels
      assert is_sequence(ndupe_l) and len(ndupe_l) <= nlevels
      assert names is None or names is False or names is True or len(names) is nlevels
      assert idx_type is None or (
          idx_type in ("i", "f", "s", "u", "dt", "p", "td") and nlevels == 1
      )
  
      if names is True:
          # build default names
          names = [prefix + str(i) for i in range(nlevels)]
      if names is False:
          # pass None to index constructor for no name
          names = None
  
      # make singleton case uniform
      if isinstance(names, str) and nlevels == 1:
          names = [names]
  
      # specific 1D index type requested?
      idx_func_dict: dict[str, Callable[..., Index]] = {
          "i": makeIntIndex,
          "f": makeFloatIndex,
          "s": makeStringIndex,
          "u": makeUnicodeIndex,
          "dt": makeDateIndex,
          "td": makeTimedeltaIndex,
          "p": makePeriodIndex,
      }
      idx_func = idx_func_dict.get(idx_type)
      if idx_func:
          idx = idx_func(nentries)
          # but we need to fill in the name
          if names:
              idx.name = names[0]
          return idx
      elif idx_type is not None:
          raise ValueError(
              f"{repr(idx_type)} is not a legal value for `idx_type`, "
              "use  'i'/'f'/'s'/'u'/'dt'/'p'/'td'."
          )
  
      if len(ndupe_l) < nlevels:
          ndupe_l.extend([1] * (nlevels - len(ndupe_l)))
      assert len(ndupe_l) == nlevels
  
      assert all(x > 0 for x in ndupe_l)
  
      list_of_lists = []
      for i in range(nlevels):
  
          def keyfunc(x):
              import re
  
              numeric_tuple = re.sub(r"[^\d_]_?", "", x).split("_")
              return [int(num) for num in numeric_tuple]
  
          # build a list of lists to create the index from
          div_factor = nentries // ndupe_l[i] + 1
  
          # Deprecated since version 3.9: collections.Counter now supports []. See PEP 585
          # and Generic Alias Type.
          cnt: Counter[str] = collections.Counter()
          for j in range(div_factor):
              label = f"{prefix}_l{i}_g{j}"
              cnt[label] = ndupe_l[i]
          # cute Counter trick
          result = sorted(cnt.elements(), key=keyfunc)[:nentries]
          list_of_lists.append(result)
  
      tuples = list(zip(*list_of_lists))
  
      # convert tuples to index
      if nentries == 1:
          # we have a single level of tuples, i.e. a regular Index
          index = Index(tuples[0], name=names[0])
      elif nlevels == 1:
          name = None if names is None else names[0]
          index = Index((x[0] for x in tuples), name=name)
      else:
          index = MultiIndex.from_tuples(tuples, names=names)
      return index
  
  
  def makeCustomDataframe(
      nrows,
      ncols,
      c_idx_names=True,
      r_idx_names=True,
      c_idx_nlevels=1,
      r_idx_nlevels=1,
      data_gen_f=None,
      c_ndupe_l=None,
      r_ndupe_l=None,
      dtype=None,
      c_idx_type=None,
      r_idx_type=None,
  ):
      """
      Create a DataFrame using supplied parameters.
  
      Parameters
      ----------
      nrows,  ncols - number of data rows/cols
      c_idx_names, idx_names  - False/True/list of strings,  yields No names ,
              default names or uses the provided names for the levels of the
              corresponding index. You can provide a single string when
              c_idx_nlevels ==1.
      c_idx_nlevels - number of levels in columns index. > 1 will yield MultiIndex
      r_idx_nlevels - number of levels in rows index. > 1 will yield MultiIndex
      data_gen_f - a function f(row,col) which return the data value
              at that position, the default generator used yields values of the form
              "RxCy" based on position.
      c_ndupe_l, r_ndupe_l - list of integers, determines the number
              of duplicates for each label at a given level of the corresponding
              index. The default `None` value produces a multiplicity of 1 across
              all levels, i.e. a unique index. Will accept a partial list of length
              N < idx_nlevels, for just the first N levels. If ndupe doesn't divide
              nrows/ncol, the last label might have lower multiplicity.
      dtype - passed to the DataFrame constructor as is, in case you wish to
              have more control in conjunction with a custom `data_gen_f`
      r_idx_type, c_idx_type -  "i"/"f"/"s"/"u"/"dt"/"td".
          If idx_type is not None, `idx_nlevels` must be 1.
          "i"/"f" creates an integer/float index,
          "s"/"u" creates a string/unicode index
          "dt" create a datetime index.
          "td" create a timedelta index.
  
              if unspecified, string labels will be generated.
  
      Examples
      --------
      # 5 row, 3 columns, default names on both, single index on both axis
      >> makeCustomDataframe(5,3)
  
      # make the data a random int between 1 and 100
      >> mkdf(5,3,data_gen_f=lambda r,c:randint(1,100))
  
      # 2-level multiindex on rows with each label duplicated
      # twice on first level, default names on both axis, single
      # index on both axis
      >> a=makeCustomDataframe(5,3,r_idx_nlevels=2,r_ndupe_l=[2])
  
      # DatetimeIndex on row, index with unicode labels on columns
      # no names on either axis
      >> a=makeCustomDataframe(5,3,c_idx_names=False,r_idx_names=False,
                               r_idx_type="dt",c_idx_type="u")
  
      # 4-level multindex on rows with names provided, 2-level multindex
      # on columns with default labels and default names.
      >> a=makeCustomDataframe(5,3,r_idx_nlevels=4,
                               r_idx_names=["FEE","FIH","FOH","FUM"],
                               c_idx_nlevels=2)
  
      >> a=mkdf(5,3,r_idx_nlevels=2,c_idx_nlevels=4)
      """
      assert c_idx_nlevels > 0
      assert r_idx_nlevels > 0
      assert r_idx_type is None or (
          r_idx_type in ("i", "f", "s", "u", "dt", "p", "td") and r_idx_nlevels == 1
      )
      assert c_idx_type is None or (
          c_idx_type in ("i", "f", "s", "u", "dt", "p", "td") and c_idx_nlevels == 1
      )
  
      columns = makeCustomIndex(
          ncols,
          nlevels=c_idx_nlevels,
          prefix="C",
          names=c_idx_names,
          ndupe_l=c_ndupe_l,
          idx_type=c_idx_type,
      )
      index = makeCustomIndex(
          nrows,
          nlevels=r_idx_nlevels,
          prefix="R",
          names=r_idx_names,
          ndupe_l=r_ndupe_l,
          idx_type=r_idx_type,
      )
  
      # by default, generate data based on location
      if data_gen_f is None:
          data_gen_f = lambda r, c: f"R{r}C{c}"
  
      data = [[data_gen_f(r, c) for c in range(ncols)] for r in range(nrows)]
  
      return DataFrame(data, index, columns, dtype=dtype)
  
  
  def _create_missing_idx(nrows, ncols, density, random_state=None):
      if random_state is None:
          random_state = np.random
      else:
          random_state = np.random.RandomState(random_state)
  
      # below is cribbed from scipy.sparse
      size = round((1 - density) * nrows * ncols)
      # generate a few more to ensure unique values
      min_rows = 5
      fac = 1.02
      extra_size = min(size + min_rows, fac * size)
  
      def _gen_unique_rand(rng, _extra_size):
          ind = rng.rand(int(_extra_size))
          return np.unique(np.floor(ind * nrows * ncols))[:size]
  
      ind = _gen_unique_rand(random_state, extra_size)
      while ind.size < size:
          extra_size *= 1.05
          ind = _gen_unique_rand(random_state, extra_size)
  
      j = np.floor(ind * 1.0 / nrows).astype(int)
      i = (ind - j * nrows).astype(int)
      return i.tolist(), j.tolist()
  
  
  def makeMissingDataframe(density=0.9, random_state=None):
      df = makeDataFrame()
      i, j = _create_missing_idx(*df.shape, density=density, random_state=random_state)
      df.values[i, j] = np.nan
      return df
  
  
  def test_parallel(num_threads=2, kwargs_list=None):
      """
      Decorator to run the same function multiple times in parallel.
  
      Parameters
      ----------
      num_threads : int, optional
          The number of times the function is run in parallel.
      kwargs_list : list of dicts, optional
          The list of kwargs to update original
          function kwargs on different threads.
  
      Notes
      -----
      This decorator does not pass the return value of the decorated function.
  
      Original from scikit-image:
  
      https://github.com/scikit-image/scikit-image/pull/1519
  
      """
      assert num_threads > 0
      has_kwargs_list = kwargs_list is not None
      if has_kwargs_list:
          assert len(kwargs_list) == num_threads
      import threading
  
      def wrapper(func):
          @wraps(func)
          def inner(*args, **kwargs):
              if has_kwargs_list:
                  update_kwargs = lambda i: dict(kwargs, **kwargs_list[i])
              else:
                  update_kwargs = lambda i: kwargs
              threads = []
              for i in range(num_threads):
                  updated_kwargs = update_kwargs(i)
                  thread = threading.Thread(target=func, args=args, kwargs=updated_kwargs)
                  threads.append(thread)
              for thread in threads:
                  thread.start()
              for thread in threads:
                  thread.join()
  
          return inner
  
      return wrapper
  
  
  class SubclassedSeries(Series):
      _metadata = ["testattr", "name"]
  
      @property
      def _constructor(self):
          return SubclassedSeries
  
      @property
      def _constructor_expanddim(self):
          return SubclassedDataFrame
  
  
  class SubclassedDataFrame(DataFrame):
      _metadata = ["testattr"]
  
      @property
      def _constructor(self):
          return SubclassedDataFrame
  
      @property
      def _constructor_sliced(self):
          return SubclassedSeries
  
  
  class SubclassedCategorical(Categorical):
      @property
      def _constructor(self):
          return SubclassedCategorical
  
  
  def _make_skipna_wrapper(alternative, skipna_alternative=None):
      """
      Create a function for calling on an array.
  
      Parameters
      ----------
      alternative : function
          The function to be called on the array with no NaNs.
          Only used when 'skipna_alternative' is None.
      skipna_alternative : function
          The function to be called on the original array
  
      Returns
      -------
      function
      """
      if skipna_alternative:
  
          def skipna_wrapper(x):
              return skipna_alternative(x.values)
  
      else:
  
          def skipna_wrapper(x):
              nona = x.dropna()
              if len(nona) == 0:
                  return np.nan
              return alternative(nona)
  
      return skipna_wrapper
  
  
  def convert_rows_list_to_csv_str(rows_list: list[str]):
      """
      Convert list of CSV rows to single CSV-formatted string for current OS.
  
      This method is used for creating expected value of to_csv() method.
  
      Parameters
      ----------
      rows_list : List[str]
          Each element represents the row of csv.
  
      Returns
      -------
      str
          Expected output of to_csv() in current OS.
      """
      sep = os.linesep
      return sep.join(rows_list) + sep
  
  
  def external_error_raised(expected_exception: type[Exception]) -> ContextManager:
      """
      Helper function to mark pytest.raises that have an external error message.
  
      Parameters
      ----------
      expected_exception : Exception
          Expected error to raise.
  
      Returns
      -------
      Callable
          Regular `pytest.raises` function with `match` equal to `None`.
      """
      import pytest
  
      return pytest.raises(expected_exception, match=None)  # noqa: PDF010
  
  
  cython_table = pd.core.common._cython_table.items()
  
  
  def get_cython_table_params(ndframe, func_names_and_expected):
      """
      Combine frame, functions from com._cython_table
      keys and expected result.
  
      Parameters
      ----------
      ndframe : DataFrame or Series
      func_names_and_expected : Sequence of two items
          The first item is a name of a NDFrame method ('sum', 'prod') etc.
          The second item is the expected return value.
  
      Returns
      -------
      list
          List of three items (DataFrame, function, expected result)
      """
      results = []
      for func_name, expected in func_names_and_expected:
          results.append((ndframe, func_name, expected))
          results += [
              (ndframe, func, expected)
              for func, name in cython_table
              if name == func_name
          ]
      return results
  
  
  def get_op_from_name(op_name: str) -> Callable:
      """
      The operator function for a given op name.
  
      Parameters
      ----------
      op_name : str
          The op name, in form of "add" or "__add__".
  
      Returns
      -------
      function
          A function performing the operation.
      """
      short_opname = op_name.strip("_")
      try:
          op = getattr(operator, short_opname)
      except AttributeError:
          # Assume it is the reverse operator
          rop = getattr(operator, short_opname[1:])
          op = lambda x, y: rop(y, x)
  
      return op
  
  
  # -----------------------------------------------------------------------------
  # Indexing test helpers
  
  
  def getitem(x):
      return x
  
  
  def setitem(x):
      return x
  
  
  def loc(x):
      return x.loc
  
  
  def iloc(x):
      return x.iloc
  
  
  def at(x):
      return x.at
  
  
  def iat(x):
      return x.iat
  
  
  # -----------------------------------------------------------------------------
  
  
  def shares_memory(left, right) -> bool:
      """
      Pandas-compat for np.shares_memory.
      """
      if isinstance(left, np.ndarray) and isinstance(right, np.ndarray):
          return np.shares_memory(left, right)
      elif isinstance(left, np.ndarray):
          # Call with reversed args to get to unpacking logic below.
          return shares_memory(right, left)
  
      if isinstance(left, RangeIndex):
          return False
      if isinstance(left, MultiIndex):
          return shares_memory(left._codes, right)
      if isinstance(left, (Index, Series)):
          return shares_memory(left._values, right)
  
      if isinstance(left, NDArrayBackedExtensionArray):
          return shares_memory(left._ndarray, right)
      if isinstance(left, pd.core.arrays.SparseArray):
          return shares_memory(left.sp_values, right)
      if isinstance(left, pd.core.arrays.IntervalArray):
          return shares_memory(left._left, right) or shares_memory(left._right, right)
  
      if isinstance(left, ExtensionArray) and left.dtype == "string[pyarrow]":
          # https://github.com/pandas-dev/pandas/pull/43930#discussion_r736862669
          if isinstance(right, ExtensionArray) and right.dtype == "string[pyarrow]":
              # error: "ExtensionArray" has no attribute "_data"
              left_pa_data = left._data  # type: ignore[attr-defined]
              # error: "ExtensionArray" has no attribute "_data"
              right_pa_data = right._data  # type: ignore[attr-defined]
              left_buf1 = left_pa_data.chunk(0).buffers()[1]
              right_buf1 = right_pa_data.chunk(0).buffers()[1]
              return left_buf1 == right_buf1
  
      if isinstance(left, BaseMaskedArray) and isinstance(right, BaseMaskedArray):
          # By convention, we'll say these share memory if they share *either*
          #  the _data or the _mask
          return np.shares_memory(left._data, right._data) or np.shares_memory(
              left._mask, right._mask
          )
  
      if isinstance(left, DataFrame) and len(left._mgr.arrays) == 1:
          arr = left._mgr.arrays[0]
          return shares_memory(arr, right)
  
      raise NotImplementedError(type(left), type(right))
  