code: from __future__ import division
  # pylint: disable-msg=W0402
  
  import re
  import string
  import sys
  import tempfile
  import warnings
  import inspect
  import os
  import subprocess
  import locale
  import traceback
  
  from datetime import datetime
  from functools import wraps
  from contextlib import contextmanager
  
  from numpy.random import randn, rand
  import numpy as np
  
  import pandas as pd
  from pandas.core.arrays import ExtensionArray
  from pandas.core.dtypes.missing import array_equivalent
  from pandas.core.dtypes.common import (
      is_datetimelike_v_numeric,
      is_datetimelike_v_object,
      is_number, is_bool,
      needs_i8_conversion,
      is_categorical_dtype,
      is_interval_dtype,
      is_sequence,
      is_list_like)
  from pandas.io.formats.printing import pprint_thing
  from pandas.core.algorithms import take_1d
  import pandas.core.common as com
  
  import pandas.compat as compat
  from pandas.compat import (
      filter, map, zip, range, unichr, lrange, lmap, lzip, u, callable, Counter,
      raise_with_traceback, httplib, StringIO, PY3)
  
  from pandas import (bdate_range, CategoricalIndex, Categorical, IntervalIndex,
                      DatetimeIndex, TimedeltaIndex, PeriodIndex, RangeIndex,
                      Index, MultiIndex,
                      Series, DataFrame, Panel)
  
  from pandas._libs import testing as _testing
  from pandas.io.common import urlopen
  
  
  N = 30
  K = 4
  _RAISE_NETWORK_ERROR_DEFAULT = False
  
  # set testing_mode
  _testing_mode_warnings = (DeprecationWarning, compat.ResourceWarning)
  
  
  def set_testing_mode():
      # set the testing mode filters
      testing_mode = os.environ.get('PANDAS_TESTING_MODE', 'None')
      if 'deprecate' in testing_mode:
          warnings.simplefilter('always', _testing_mode_warnings)
  
  
  def reset_testing_mode():
      # reset the testing mode filters
      testing_mode = os.environ.get('PANDAS_TESTING_MODE', 'None')
      if 'deprecate' in testing_mode:
          warnings.simplefilter('ignore', _testing_mode_warnings)
  
  
  set_testing_mode()
  
  
  def reset_display_options():
      """
      Reset the display options for printing and representing objects.
      """
  
      pd.reset_option('^display.', silent=True)
  
  
  def round_trip_pickle(obj, path=None):
      """
      Pickle an object and then read it again.
  
      Parameters
      ----------
      obj : pandas object
          The object to pickle and then re-read.
      path : str, default None
          The path where the pickled object is written and then read.
  
      Returns
      -------
      round_trip_pickled_object : pandas object
          The original object that was pickled and then re-read.
      """
  
      if path is None:
          path = u('__{random_bytes}__.pickle'.format(random_bytes=rands(10)))
      with ensure_clean(path) as path:
          pd.to_pickle(obj, path)
          return pd.read_pickle(path)
  
  
  def round_trip_pathlib(writer, reader, path=None):
      """
      Write an object to file specified by a pathlib.Path and read it back
  
      Parameters
      ----------
      writer : callable bound to pandas object
          IO writing function (e.g. DataFrame.to_csv )
      reader : callable
          IO reading function (e.g. pd.read_csv )
      path : str, default None
          The path where the object is written and then read.
  
      Returns
      -------
      round_trip_object : pandas object
          The original object that was serialized and then re-read.
      """
  
      import pytest
      Path = pytest.importorskip('pathlib').Path
      if path is None:
          path = '___pathlib___'
      with ensure_clean(path) as path:
          writer(Path(path))
          obj = reader(Path(path))
      return obj
  
  
  def round_trip_localpath(writer, reader, path=None):
      """
      Write an object to file specified by a py.path LocalPath and read it back
  
      Parameters
      ----------
      writer : callable bound to pandas object
          IO writing function (e.g. DataFrame.to_csv )
      reader : callable
          IO reading function (e.g. pd.read_csv )
      path : str, default None
          The path where the object is written and then read.
  
      Returns
      -------
      round_trip_object : pandas object
          The original object that was serialized and then re-read.
      """
      import pytest
      LocalPath = pytest.importorskip('py.path').local
      if path is None:
          path = '___localpath___'
      with ensure_clean(path) as path:
          writer(LocalPath(path))
          obj = reader(LocalPath(path))
      return obj
  
  
  @contextmanager
  def decompress_file(path, compression):
      """
      Open a compressed file and return a file object
  
      Parameters
      ----------
      path : str
          The path where the file is read from
  
      compression : {'gzip', 'bz2', 'zip', 'xz', None}
          Name of the decompression to use
  
      Returns
      -------
      f : file object
      """
  
      if compression is None:
          f = open(path, 'rb')
      elif compression == 'gzip':
          import gzip
          f = gzip.open(path, 'rb')
      elif compression == 'bz2':
          import bz2
          f = bz2.BZ2File(path, 'rb')
      elif compression == 'xz':
          lzma = compat.import_lzma()
          f = lzma.LZMAFile(path, 'rb')
      elif compression == 'zip':
          import zipfile
          zip_file = zipfile.ZipFile(path)
          zip_names = zip_file.namelist()
          if len(zip_names) == 1:
              f = zip_file.open(zip_names.pop())
          else:
              raise ValueError('ZIP file {} error. Only one file per ZIP.'
                               .format(path))
      else:
          msg = 'Unrecognized compression type: {}'.format(compression)
          raise ValueError(msg)
  
      yield f
      f.close()
  
  
  def assert_almost_equal(left, right, check_exact=False,
                          check_dtype='equiv', check_less_precise=False,
                          **kwargs):
      """
      Check that the left and right objects are approximately equal.
  
      Parameters
      ----------
      left : object
      right : object
      check_exact : bool, default False
          Whether to compare number exactly.
      check_dtype: bool, default True
          check dtype if both a and b are the same type
      check_less_precise : bool or int, default False
          Specify comparison precision. Only used when check_exact is False.
          5 digits (False) or 3 digits (True) after decimal points are compared.
          If int, then specify the digits to compare
      """
      if isinstance(left, pd.Index):
          return assert_index_equal(left, right, check_exact=check_exact,
                                    exact=check_dtype,
                                    check_less_precise=check_less_precise,
                                    **kwargs)
  
      elif isinstance(left, pd.Series):
          return assert_series_equal(left, right, check_exact=check_exact,
                                     check_dtype=check_dtype,
                                     check_less_precise=check_less_precise,
                                     **kwargs)
  
      elif isinstance(left, pd.DataFrame):
          return assert_frame_equal(left, right, check_exact=check_exact,
                                    check_dtype=check_dtype,
                                    check_less_precise=check_less_precise,
                                    **kwargs)
  
      else:
          # other sequences
          if check_dtype:
              if is_number(left) and is_number(right):
                  # do not compare numeric classes, like np.float64 and float
                  pass
              elif is_bool(left) and is_bool(right):
                  # do not compare bool classes, like np.bool_ and bool
                  pass
              else:
                  if (isinstance(left, np.ndarray) or
                          isinstance(right, np.ndarray)):
                      obj = 'numpy array'
                  else:
                      obj = 'Input'
                  assert_class_equal(left, right, obj=obj)
          return _testing.assert_almost_equal(
              left, right,
              check_dtype=check_dtype,
              check_less_precise=check_less_precise,
              **kwargs)
  
  
  def _check_isinstance(left, right, cls):
      """
      Helper method for our assert_* methods that ensures that
      the two objects being compared have the right type before
      proceeding with the comparison.
  
      Parameters
      ----------
      left : The first object being compared.
      right : The second object being compared.
      cls : The class type to check against.
  
      Raises
      ------
      AssertionError : Either `left` or `right` is not an instance of `cls`.
      """
  
      err_msg = "{name} Expected type {exp_type}, found {act_type} instead"
      cls_name = cls.__name__
  
      if not isinstance(left, cls):
          raise AssertionError(err_msg.format(name=cls_name, exp_type=cls,
                                              act_type=type(left)))
      if not isinstance(right, cls):
          raise AssertionError(err_msg.format(name=cls_name, exp_type=cls,
                                              act_type=type(right)))
  
  
  def assert_dict_equal(left, right, compare_keys=True):
  
      _check_isinstance(left, right, dict)
      return _testing.assert_dict_equal(left, right, compare_keys=compare_keys)
  
  
  def randbool(size=(), p=0.5):
      return rand(*size) <= p
  
  
  RANDS_CHARS = np.array(list(string.ascii_letters + string.digits),
                         dtype=(np.str_, 1))
  RANDU_CHARS = np.array(list(u("").join(map(unichr, lrange(1488, 1488 + 26))) +
                              string.digits), dtype=(np.unicode_, 1))
  
  
  def rands_array(nchars, size, dtype='O'):
      """Generate an array of byte strings."""
      retval = (np.random.choice(RANDS_CHARS, size=nchars * np.prod(size))
                .view((np.str_, nchars)).reshape(size))
      if dtype is None:
          return retval
      else:
          return retval.astype(dtype)
  
  
  def randu_array(nchars, size, dtype='O'):
      """Generate an array of unicode strings."""
      retval = (np.random.choice(RANDU_CHARS, size=nchars * np.prod(size))
                .view((np.unicode_, nchars)).reshape(size))
      if dtype is None:
          return retval
      else:
          return retval.astype(dtype)
  
  
  def rands(nchars):
      """
      Generate one random byte string.
  
      See `rands_array` if you want to create an array of random strings.
  
      """
      return ''.join(np.random.choice(RANDS_CHARS, nchars))
  
  
  def randu(nchars):
      """
      Generate one random unicode string.
  
      See `randu_array` if you want to create an array of random unicode strings.
  
      """
      return ''.join(np.random.choice(RANDU_CHARS, nchars))
  
  
  def close(fignum=None):
      from matplotlib.pyplot import get_fignums, close as _close
  
      if fignum is None:
          for fignum in get_fignums():
              _close(fignum)
      else:
          _close(fignum)
  
  
  # -----------------------------------------------------------------------------
  # locale utilities
  
  
  def check_output(*popenargs, **kwargs):
      # shamelessly taken from Python 2.7 source
      r"""Run command with arguments and return its output as a byte string.
  
      If the exit code was non-zero it raises a CalledProcessError.  The
      CalledProcessError object will have the return code in the returncode
      attribute and output in the output attribute.
  
      The arguments are the same as for the Popen constructor.  Example:
  
      >>> check_output(["ls", "-l", "/dev/null"])
      'crw-rw-rw- 1 root root 1, 3 Oct 18  2007 /dev/null\n'
  
      The stdout argument is not allowed as it is used internally.
      To capture standard error in the result, use stderr=STDOUT.
  
      >>> check_output(["/bin/sh", "-c",
      ...               "ls -l non_existent_file ; exit 0"],
      ...              stderr=STDOUT)
      'ls: non_existent_file: No such file or directory\n'
      """
      if 'stdout' in kwargs:
          raise ValueError('stdout argument not allowed, it will be overridden.')
      process = subprocess.Popen(stdout=subprocess.PIPE, stderr=subprocess.PIPE,
                                 *popenargs, **kwargs)
      output, unused_err = process.communicate()
      retcode = process.poll()
      if retcode:
          cmd = kwargs.get("args")
          if cmd is None:
              cmd = popenargs[0]
          raise subprocess.CalledProcessError(retcode, cmd, output=output)
      return output
  
  
  def _default_locale_getter():
      try:
          raw_locales = check_output(['locale -a'], shell=True)
      except subprocess.CalledProcessError as e:
          raise type(e)("{exception}, the 'locale -a' command cannot be found "
                        "on your system".format(exception=e))
      return raw_locales
  
  
  def get_locales(prefix=None, normalize=True,
                  locale_getter=_default_locale_getter):
      """Get all the locales that are available on the system.
  
      Parameters
      ----------
      prefix : str
          If not ``None`` then return only those locales with the prefix
          provided. For example to get all English language locales (those that
          start with ``"en"``), pass ``prefix="en"``.
      normalize : bool
          Call ``locale.normalize`` on the resulting list of available locales.
          If ``True``, only locales that can be set without throwing an
          ``Exception`` are returned.
      locale_getter : callable
          The function to use to retrieve the current locales. This should return
          a string with each locale separated by a newline character.
  
      Returns
      -------
      locales : list of strings
          A list of locale strings that can be set with ``locale.setlocale()``.
          For example::
  
              locale.setlocale(locale.LC_ALL, locale_string)
  
      On error will return None (no locale available, e.g. Windows)
  
      """
      try:
          raw_locales = locale_getter()
      except Exception:
          return None
  
      try:
          # raw_locales is "\n" separated list of locales
          # it may contain non-decodable parts, so split
          # extract what we can and then rejoin.
          raw_locales = raw_locales.split(b'\n')
          out_locales = []
          for x in raw_locales:
              if PY3:
                  out_locales.append(str(
                      x, encoding=pd.options.display.encoding))
              else:
                  out_locales.append(str(x))
  
      except TypeError:
          pass
  
      if prefix is None:
          return _valid_locales(out_locales, normalize)
  
      found = re.compile('{prefix}.*'.format(prefix=prefix)) \
                .findall('\n'.join(out_locales))
      return _valid_locales(found, normalize)
  
  
  @contextmanager
  def set_locale(new_locale, lc_var=locale.LC_ALL):
      """Context manager for temporarily setting a locale.
  
      Parameters
      ----------
      new_locale : str or tuple
          A string of the form <language_country>.<encoding>. For example to set
          the current locale to US English with a UTF8 encoding, you would pass
          "en_US.UTF-8".
  
      Notes
      -----
      This is useful when you want to run a particular block of code under a
      particular locale, without globally setting the locale. This probably isn't
      thread-safe.
      """
      current_locale = locale.getlocale()
  
      try:
          locale.setlocale(lc_var, new_locale)
  
          try:
              normalized_locale = locale.getlocale()
          except ValueError:
              yield new_locale
          else:
              if com._all_not_none(*normalized_locale):
                  yield '.'.join(normalized_locale)
              else:
                  yield new_locale
      finally:
          locale.setlocale(lc_var, current_locale)
  
  
  def _can_set_locale(lc):
      """Check to see if we can set a locale without throwing an exception.
  
      Parameters
      ----------
      lc : str
          The locale to attempt to set.
  
      Returns
      -------
      isvalid : bool
          Whether the passed locale can be set
      """
      try:
          with set_locale(lc):
              pass
      except locale.Error:  # horrible name for a Exception subclass
          return False
      else:
          return True
  
  
  def _valid_locales(locales, normalize):
      """Return a list of normalized locales that do not throw an ``Exception``
      when set.
  
      Parameters
      ----------
      locales : str
          A string where each locale is separated by a newline.
      normalize : bool
          Whether to call ``locale.normalize`` on each locale.
  
      Returns
      -------
      valid_locales : list
          A list of valid locales.
      """
      if normalize:
          normalizer = lambda x: locale.normalize(x.strip())
      else:
          normalizer = lambda x: x.strip()
  
      return list(filter(_can_set_locale, map(normalizer, locales)))
  
  # -----------------------------------------------------------------------------
  # Stdout / stderr decorators
  
  
  def capture_stdout(f):
      """
      Decorator to capture stdout in a buffer so that it can be checked
      (or suppressed) during testing.
  
      Parameters
      ----------
      f : callable
          The test that is capturing stdout.
  
      Returns
      -------
      f : callable
          The decorated test ``f``, which captures stdout.
  
      Examples
      --------
  
      >>> from pandas.util.testing import capture_stdout
      >>>
      >>> import sys
      >>>
      >>> @capture_stdout
      ... def test_print_pass():
      ...     print("foo")
      ...     out = sys.stdout.getvalue()
      ...     assert out == "foo\n"
      >>>
      >>> @capture_stdout
      ... def test_print_fail():
      ...     print("foo")
      ...     out = sys.stdout.getvalue()
      ...     assert out == "bar\n"
      ...
      AssertionError: assert 'foo\n' == 'bar\n'
      """
  
      @wraps(f)
      def wrapper(*args, **kwargs):
          try:
              sys.stdout = StringIO()
              f(*args, **kwargs)
          finally:
              sys.stdout = sys.__stdout__
  
      return wrapper
  
  
  def capture_stderr(f):
      """
      Decorator to capture stderr in a buffer so that it can be checked
      (or suppressed) during testing.
  
      Parameters
      ----------
      f : callable
          The test that is capturing stderr.
  
      Returns
      -------
      f : callable
          The decorated test ``f``, which captures stderr.
  
      Examples
      --------
  
      >>> from pandas.util.testing import capture_stderr
      >>>
      >>> import sys
      >>>
      >>> @capture_stderr
      ... def test_stderr_pass():
      ...     sys.stderr.write("foo")
      ...     out = sys.stderr.getvalue()
      ...     assert out == "foo\n"
      >>>
      >>> @capture_stderr
      ... def test_stderr_fail():
      ...     sys.stderr.write("foo")
      ...     out = sys.stderr.getvalue()
      ...     assert out == "bar\n"
      ...
      AssertionError: assert 'foo\n' == 'bar\n'
      """
  
      @wraps(f)
      def wrapper(*args, **kwargs):
          try:
              sys.stderr = StringIO()
              f(*args, **kwargs)
          finally:
              sys.stderr = sys.__stderr__
  
      return wrapper
  
  # -----------------------------------------------------------------------------
  # Console debugging tools
  
  
  def debug(f, *args, **kwargs):
      from pdb import Pdb as OldPdb
      try:
          from IPython.core.debugger import Pdb
          kw = dict(color_scheme='Linux')
      except ImportError:
          Pdb = OldPdb
          kw = {}
      pdb = Pdb(**kw)
      return pdb.runcall(f, *args, **kwargs)
  
  
  def pudebug(f, *args, **kwargs):
      import pudb
      return pudb.runcall(f, *args, **kwargs)
  
  
  def set_trace():
      from IPython.core.debugger import Pdb
      try:
          Pdb(color_scheme='Linux').set_trace(sys._getframe().f_back)
      except Exception:
          from pdb import Pdb as OldPdb
          OldPdb().set_trace(sys._getframe().f_back)
  
  # -----------------------------------------------------------------------------
  # contextmanager to ensure the file cleanup
  
  
  @contextmanager
  def ensure_clean(filename=None, return_filelike=False):
      """Gets a temporary path and agrees to remove on close.
  
      Parameters
      ----------
      filename : str (optional)
          if None, creates a temporary file which is then removed when out of
          scope. if passed, creates temporary file with filename as ending.
      return_filelike : bool (default False)
          if True, returns a file-like which is *always* cleaned. Necessary for
          savefig and other functions which want to append extensions.
      """
      filename = filename or ''
      fd = None
  
      if return_filelike:
          f = tempfile.TemporaryFile(suffix=filename)
          try:
              yield f
          finally:
              f.close()
      else:
          # don't generate tempfile if using a path with directory specified
          if len(os.path.dirname(filename)):
              raise ValueError("Can't pass a qualified name to ensure_clean()")
  
          try:
              fd, filename = tempfile.mkstemp(suffix=filename)
          except UnicodeEncodeError:
              import pytest
              pytest.skip('no unicode file names on this system')
  
          try:
              yield filename
          finally:
              try:
                  os.close(fd)
              except Exception as e:
                  print("Couldn't close file descriptor: {fdesc} (file: {fname})"
                        .format(fdesc=fd, fname=filename))
              try:
                  if os.path.exists(filename):
                      os.remove(filename)
              except Exception as e:
                  print("Exception on removing file: {error}".format(error=e))
  
  
  def get_data_path(f=''):
      """Return the path of a data file, these are relative to the current test
      directory.
      """
      # get our callers file
      _, filename, _, _, _, _ = inspect.getouterframes(inspect.currentframe())[1]
      base_dir = os.path.abspath(os.path.dirname(filename))
      return os.path.join(base_dir, 'data', f)
  
  # -----------------------------------------------------------------------------
  # Comparators
  
  
  def equalContents(arr1, arr2):
      """Checks if the set of unique elements of arr1 and arr2 are equivalent.
      """
      return frozenset(arr1) == frozenset(arr2)
  
  
  def assert_index_equal(left, right, exact='equiv', check_names=True,
                         check_less_precise=False, check_exact=True,
                         check_categorical=True, obj='Index'):
      """Check that left and right Index are equal.
  
      Parameters
      ----------
      left : Index
      right : Index
      exact : bool / string {'equiv'}, default False
          Whether to check the Index class, dtype and inferred_type
          are identical. If 'equiv', then RangeIndex can be substituted for
          Int64Index as well.
      check_names : bool, default True
          Whether to check the names attribute.
      check_less_precise : bool or int, default False
          Specify comparison precision. Only used when check_exact is False.
          5 digits (False) or 3 digits (True) after decimal points are compared.
          If int, then specify the digits to compare
      check_exact : bool, default True
          Whether to compare number exactly.
      check_categorical : bool, default True
          Whether to compare internal Categorical exactly.
      obj : str, default 'Index'
          Specify object name being compared, internally used to show appropriate
          assertion message
      """
  
      def _check_types(l, r, obj='Index'):
          if exact:
              assert_class_equal(left, right, exact=exact, obj=obj)
              assert_attr_equal('dtype', l, r, obj=obj)
              # allow string-like to have different inferred_types
              if l.inferred_type in ('string', 'unicode'):
                  assert r.inferred_type in ('string', 'unicode')
              else:
                  assert_attr_equal('inferred_type', l, r, obj=obj)
  
      def _get_ilevel_values(index, level):
          # accept level number only
          unique = index.levels[level]
          labels = index.labels[level]
          filled = take_1d(unique.values, labels, fill_value=unique._na_value)
          values = unique._shallow_copy(filled, name=index.names[level])
          return values
  
      # instance validation
      _check_isinstance(left, right, Index)
  
      # class / dtype comparison
      _check_types(left, right, obj=obj)
  
      # level comparison
      if left.nlevels != right.nlevels:
          msg1 = '{obj} levels are different'.format(obj=obj)
          msg2 = '{nlevels}, {left}'.format(nlevels=left.nlevels, left=left)
          msg3 = '{nlevels}, {right}'.format(nlevels=right.nlevels, right=right)
          raise_assert_detail(obj, msg1, msg2, msg3)
  
      # length comparison
      if len(left) != len(right):
          msg1 = '{obj} length are different'.format(obj=obj)
          msg2 = '{length}, {left}'.format(length=len(left), left=left)
          msg3 = '{length}, {right}'.format(length=len(right), right=right)
          raise_assert_detail(obj, msg1, msg2, msg3)
  
      # MultiIndex special comparison for little-friendly error messages
      if left.nlevels > 1:
          for level in range(left.nlevels):
              # cannot use get_level_values here because it can change dtype
              llevel = _get_ilevel_values(left, level)
              rlevel = _get_ilevel_values(right, level)
  
              lobj = 'MultiIndex level [{level}]'.format(level=level)
              assert_index_equal(llevel, rlevel,
                                 exact=exact, check_names=check_names,
                                 check_less_precise=check_less_precise,
                                 check_exact=check_exact, obj=lobj)
              # get_level_values may change dtype
              _check_types(left.levels[level], right.levels[level], obj=obj)
  
      if check_exact:
          if not left.equals(right):
              diff = np.sum((left.values != right.values)
                            .astype(int)) * 100.0 / len(left)
              msg = '{obj} values are different ({pct} %)'.format(
                  obj=obj, pct=np.round(diff, 5))
              raise_assert_detail(obj, msg, left, right)
      else:
          _testing.assert_almost_equal(left.values, right.values,
                                       check_less_precise=check_less_precise,
                                       check_dtype=exact,
                                       obj=obj, lobj=left, robj=right)
  
      # metadata comparison
      if check_names:
          assert_attr_equal('names', left, right, obj=obj)
      if isinstance(left, pd.PeriodIndex) or isinstance(right, pd.PeriodIndex):
          assert_attr_equal('freq', left, right, obj=obj)
      if (isinstance(left, pd.IntervalIndex) or
              isinstance(right, pd.IntervalIndex)):
          assert_attr_equal('closed', left, right, obj=obj)
  
      if check_categorical:
          if is_categorical_dtype(left) or is_categorical_dtype(right):
              assert_categorical_equal(left.values, right.values,
                                       obj='{obj} category'.format(obj=obj))
  
  
  def assert_class_equal(left, right, exact=True, obj='Input'):
      """checks classes are equal."""
  
      def repr_class(x):
          if isinstance(x, Index):
              # return Index as it is to include values in the error message
              return x
  
          try:
              return x.__class__.__name__
          except AttributeError:
              return repr(type(x))
  
      if exact == 'equiv':
          if type(left) != type(right):
              # allow equivalence of Int64Index/RangeIndex
              types = set([type(left).__name__, type(right).__name__])
              if len(types - set(['Int64Index', 'RangeIndex'])):
                  msg = '{obj} classes are not equivalent'.format(obj=obj)
                  raise_assert_detail(obj, msg, repr_class(left),
                                      repr_class(right))
      elif exact:
          if type(left) != type(right):
              msg = '{obj} classes are different'.format(obj=obj)
              raise_assert_detail(obj, msg, repr_class(left),
                                  repr_class(right))
  
  
  def assert_attr_equal(attr, left, right, obj='Attributes'):
      """checks attributes are equal. Both objects must have attribute.
  
      Parameters
      ----------
      attr : str
          Attribute name being compared.
      left : object
      right : object
      obj : str, default 'Attributes'
          Specify object name being compared, internally used to show appropriate
          assertion message
      """
  
      left_attr = getattr(left, attr)
      right_attr = getattr(right, attr)
  
      if left_attr is right_attr:
          return True
      elif (is_number(left_attr) and np.isnan(left_attr) and
            is_number(right_attr) and np.isnan(right_attr)):
          # np.nan
          return True
  
      try:
          result = left_attr == right_attr
      except TypeError:
          # datetimetz on rhs may raise TypeError
          result = False
      if not isinstance(result, bool):
          result = result.all()
  
      if result:
          return True
      else:
          msg = 'Attribute "{attr}" are different'.format(attr=attr)
          raise_assert_detail(obj, msg, left_attr, right_attr)
  
  
  def assert_is_valid_plot_return_object(objs):
      import matplotlib.pyplot as plt
      if isinstance(objs, (pd.Series, np.ndarray)):
          for el in objs.ravel():
              msg = ('one of \'objs\' is not a matplotlib Axes instance, type '
                     'encountered {name!r}').format(name=el.__class__.__name__)
              assert isinstance(el, (plt.Axes, dict)), msg
      else:
          assert isinstance(objs, (plt.Artist, tuple, dict)), \
              ('objs is neither an ndarray of Artist instances nor a '
               'single Artist instance, tuple, or dict, "objs" is a {name!r}'
               ).format(name=objs.__class__.__name__)
  
  
  def isiterable(obj):
      return hasattr(obj, '__iter__')
  
  
  def is_sorted(seq):
      if isinstance(seq, (Index, Series)):
          seq = seq.values
      # sorting does not change precisions
      return assert_numpy_array_equal(seq, np.sort(np.array(seq)))
  
  
  def assert_categorical_equal(left, right, check_dtype=True,
                               obj='Categorical', check_category_order=True):
      """Test that Categoricals are equivalent.
  
      Parameters
      ----------
      left, right : Categorical
          Categoricals to compare
      check_dtype : bool, default True
          Check that integer dtype of the codes are the same
      obj : str, default 'Categorical'
          Specify object name being compared, internally used to show appropriate
          assertion message
      check_category_order : bool, default True
          Whether the order of the categories should be compared, which
          implies identical integer codes.  If False, only the resulting
          values are compared.  The ordered attribute is
          checked regardless.
      """
      _check_isinstance(left, right, Categorical)
  
      if check_category_order:
          assert_index_equal(left.categories, right.categories,
                             obj='{obj}.categories'.format(obj=obj))
          assert_numpy_array_equal(left.codes, right.codes,
                                   check_dtype=check_dtype,
                                   obj='{obj}.codes'.format(obj=obj))
      else:
          assert_index_equal(left.categories.sort_values(),
                             right.categories.sort_values(),
                             obj='{obj}.categories'.format(obj=obj))
          assert_index_equal(left.categories.take(left.codes),
                             right.categories.take(right.codes),
                             obj='{obj}.values'.format(obj=obj))
  
      assert_attr_equal('ordered', left, right, obj=obj)
  
  
  def raise_assert_detail(obj, message, left, right, diff=None):
      if isinstance(left, np.ndarray):
          left = pprint_thing(left)
      elif is_categorical_dtype(left):
          left = repr(left)
      if isinstance(right, np.ndarray):
          right = pprint_thing(right)
      elif is_categorical_dtype(right):
          right = repr(right)
  
      msg = """{obj} are different
  
  {message}
  [left]:  {left}
  [right]: {right}""".format(obj=obj, message=message, left=left, right=right)
  
      if diff is not None:
          msg += "\n[diff]: {diff}".format(diff=diff)
  
      raise AssertionError(msg)
  
  
  def assert_numpy_array_equal(left, right, strict_nan=False,
                               check_dtype=True, err_msg=None,
                               obj='numpy array', check_same=None):
      """ Checks that 'np.ndarray' is equivalent
  
      Parameters
      ----------
      left : np.ndarray or iterable
      right : np.ndarray or iterable
      strict_nan : bool, default False
          If True, consider NaN and None to be different.
      check_dtype: bool, default True
          check dtype if both a and b are np.ndarray
      err_msg : str, default None
          If provided, used as assertion message
      obj : str, default 'numpy array'
          Specify object name being compared, internally used to show appropriate
          assertion message
      check_same : None|'copy'|'same', default None
          Ensure left and right refer/do not refer to the same memory area
      """
  
      # instance validation
      # Show a detailed error message when classes are different
      assert_class_equal(left, right, obj=obj)
      # both classes must be an np.ndarray
      _check_isinstance(left, right, np.ndarray)
  
      def _get_base(obj):
          return obj.base if getattr(obj, 'base', None) is not None else obj
  
      left_base = _get_base(left)
      right_base = _get_base(right)
  
      if check_same == 'same':
          if left_base is not right_base:
              msg = "{left!r} is not {right!r}".format(
                  left=left_base, right=right_base)
              raise AssertionError(msg)
      elif check_same == 'copy':
          if left_base is right_base:
              msg = "{left!r} is {right!r}".format(
                  left=left_base, right=right_base)
              raise AssertionError(msg)
  
      def _raise(left, right, err_msg):
          if err_msg is None:
              if left.shape != right.shape:
                  raise_assert_detail(obj, '{obj} shapes are different'
                                      .format(obj=obj), left.shape, right.shape)
  
              diff = 0
              for l, r in zip(left, right):
                  # count up differences
                  if not array_equivalent(l, r, strict_nan=strict_nan):
                      diff += 1
  
              diff = diff * 100.0 / left.size
              msg = '{obj} values are different ({pct} %)'.format(
                  obj=obj, pct=np.round(diff, 5))
              raise_assert_detail(obj, msg, left, right)
  
          raise AssertionError(err_msg)
  
      # compare shape and values
      if not array_equivalent(left, right, strict_nan=strict_nan):
          _raise(left, right, err_msg)
  
      if check_dtype:
          if isinstance(left, np.ndarray) and isinstance(right, np.ndarray):
              assert_attr_equal('dtype', left, right, obj=obj)
  
      return True
  
  
  def assert_extension_array_equal(left, right):
      """Check that left and right ExtensionArrays are equal.
  
      Parameters
      ----------
      left, right : ExtensionArray
          The two arrays to compare
  
      Notes
      -----
      Missing values are checked separately from valid values.
      A mask of missing values is computed for each and checked to match.
      The remaining all-valid values are cast to object dtype and checked.
      """
      assert isinstance(left, ExtensionArray)
      assert left.dtype == right.dtype
      left_na = left.isna()
      right_na = right.isna()
      assert_numpy_array_equal(left_na, right_na)
  
      left_valid = left[~left_na].astype(object)
      right_valid = right[~right_na].astype(object)
  
      assert_numpy_array_equal(left_valid, right_valid)
  
  
  # This could be refactored to use the NDFrame.equals method
  def assert_series_equal(left, right, check_dtype=True,
                          check_index_type='equiv',
                          check_series_type=True,
                          check_less_precise=False,
                          check_names=True,
                          check_exact=False,
                          check_datetimelike_compat=False,
                          check_categorical=True,
                          obj='Series'):
      """Check that left and right Series are equal.
  
      Parameters
      ----------
      left : Series
      right : Series
      check_dtype : bool, default True
          Whether to check the Series dtype is identical.
      check_index_type : bool / string {'equiv'}, default 'equiv'
          Whether to check the Index class, dtype and inferred_type
          are identical.
      check_series_type : bool, default True
          Whether to check the Series class is identical.
      check_less_precise : bool or int, default False
          Specify comparison precision. Only used when check_exact is False.
          5 digits (False) or 3 digits (True) after decimal points are compared.
          If int, then specify the digits to compare
      check_exact : bool, default False
          Whether to compare number exactly.
      check_names : bool, default True
          Whether to check the Series and Index names attribute.
      check_datetimelike_compat : bool, default False
          Compare datetime-like which is comparable ignoring dtype.
      check_categorical : bool, default True
          Whether to compare internal Categorical exactly.
      obj : str, default 'Series'
          Specify object name being compared, internally used to show appropriate
          assertion message
      """
  
      # instance validation
      _check_isinstance(left, right, Series)
  
      if check_series_type:
          # ToDo: There are some tests using rhs is sparse
          # lhs is dense. Should use assert_class_equal in future
          assert isinstance(left, type(right))
          # assert_class_equal(left, right, obj=obj)
  
      # length comparison
      if len(left) != len(right):
          msg1 = '{len}, {left}'.format(len=len(left), left=left.index)
          msg2 = '{len}, {right}'.format(len=len(right), right=right.index)
          raise_assert_detail(obj, 'Series length are different', msg1, msg2)
  
      # index comparison
      assert_index_equal(left.index, right.index, exact=check_index_type,
                         check_names=check_names,
                         check_less_precise=check_less_precise,
                         check_exact=check_exact,
                         check_categorical=check_categorical,
                         obj='{obj}.index'.format(obj=obj))
  
      if check_dtype:
          # We want to skip exact dtype checking when `check_categorical`
          # is False. We'll still raise if only one is a `Categorical`,
          # regardless of `check_categorical`
          if (is_categorical_dtype(left) and is_categorical_dtype(right) and
                  not check_categorical):
              pass
          else:
              assert_attr_equal('dtype', left, right)
  
      if check_exact:
          assert_numpy_array_equal(left.get_values(), right.get_values(),
                                   check_dtype=check_dtype,
                                   obj='{obj}'.format(obj=obj),)
      elif check_datetimelike_compat:
          # we want to check only if we have compat dtypes
          # e.g. integer and M|m are NOT compat, but we can simply check
          # the values in that case
          if (is_datetimelike_v_numeric(left, right) or
              is_datetimelike_v_object(left, right) or
              needs_i8_conversion(left) or
                  needs_i8_conversion(right)):
  
              # datetimelike may have different objects (e.g. datetime.datetime
              # vs Timestamp) but will compare equal
              if not Index(left.values).equals(Index(right.values)):
                  msg = ('[datetimelike_compat=True] {left} is not equal to '
                         '{right}.').format(left=left.values, right=right.values)
                  raise AssertionError(msg)
          else:
              assert_numpy_array_equal(left.get_values(), right.get_values(),
                                       check_dtype=check_dtype)
      elif is_interval_dtype(left) or is_interval_dtype(right):
          # TODO: big hack here
          left = pd.IntervalIndex(left)
          right = pd.IntervalIndex(right)
          assert_index_equal(left, right, obj='{obj}.index'.format(obj=obj))
  
      else:
          _testing.assert_almost_equal(left.get_values(), right.get_values(),
                                       check_less_precise=check_less_precise,
                                       check_dtype=check_dtype,
                                       obj='{obj}'.format(obj=obj))
  
      # metadata comparison
      if check_names:
          assert_attr_equal('name', left, right, obj=obj)
  
      if check_categorical:
          if is_categorical_dtype(left) or is_categorical_dtype(right):
              assert_categorical_equal(left.values, right.values,
                                       obj='{obj} category'.format(obj=obj))
  
  
  # This could be refactored to use the NDFrame.equals method
  def assert_frame_equal(left, right, check_dtype=True,
                         check_index_type='equiv',
                         check_column_type='equiv',
                         check_frame_type=True,
                         check_less_precise=False,
                         check_names=True,
                         by_blocks=False,
                         check_exact=False,
                         check_datetimelike_compat=False,
                         check_categorical=True,
                         check_like=False,
                         obj='DataFrame'):
      """Check that left and right DataFrame are equal.
  
      Parameters
      ----------
      left : DataFrame
      right : DataFrame
      check_dtype : bool, default True
          Whether to check the DataFrame dtype is identical.
      check_index_type : bool / string {'equiv'}, default False
          Whether to check the Index class, dtype and inferred_type
          are identical.
      check_column_type : bool / string {'equiv'}, default False
          Whether to check the columns class, dtype and inferred_type
          are identical.
      check_frame_type : bool, default False
          Whether to check the DataFrame class is identical.
      check_less_precise : bool or int, default False
          Specify comparison precision. Only used when check_exact is False.
          5 digits (False) or 3 digits (True) after decimal points are compared.
          If int, then specify the digits to compare
      check_names : bool, default True
          Whether to check the Index names attribute.
      by_blocks : bool, default False
          Specify how to compare internal data. If False, compare by columns.
          If True, compare by blocks.
      check_exact : bool, default False
          Whether to compare number exactly.
      check_datetimelike_compat : bool, default False
          Compare datetime-like which is comparable ignoring dtype.
      check_categorical : bool, default True
          Whether to compare internal Categorical exactly.
      check_like : bool, default False
          If true, ignore the order of rows & columns
      obj : str, default 'DataFrame'
          Specify object name being compared, internally used to show appropriate
          assertion message
      """
  
      # instance validation
      _check_isinstance(left, right, DataFrame)
  
      if check_frame_type:
          # ToDo: There are some tests using rhs is SparseDataFrame
          # lhs is DataFrame. Should use assert_class_equal in future
          assert isinstance(left, type(right))
          # assert_class_equal(left, right, obj=obj)
  
      # shape comparison
      if left.shape != right.shape:
          raise_assert_detail(obj,
                              'DataFrame shape mismatch',
                              '{shape!r}'.format(shape=left.shape),
                              '{shape!r}'.format(shape=right.shape))
  
      if check_like:
          left, right = left.reindex_like(right), right
  
      # index comparison
      assert_index_equal(left.index, right.index, exact=check_index_type,
                         check_names=check_names,
                         check_less_precise=check_less_precise,
                         check_exact=check_exact,
                         check_categorical=check_categorical,
                         obj='{obj}.index'.format(obj=obj))
  
      # column comparison
      assert_index_equal(left.columns, right.columns, exact=check_column_type,
                         check_names=check_names,
                         check_less_precise=check_less_precise,
                         check_exact=check_exact,
                         check_categorical=check_categorical,
                         obj='{obj}.columns'.format(obj=obj))
  
      # compare by blocks
      if by_blocks:
          rblocks = right._to_dict_of_blocks()
          lblocks = left._to_dict_of_blocks()
          for dtype in list(set(list(lblocks.keys()) + list(rblocks.keys()))):
              assert dtype in lblocks
              assert dtype in rblocks
              assert_frame_equal(lblocks[dtype], rblocks[dtype],
                                 check_dtype=check_dtype, obj='DataFrame.blocks')
  
      # compare by columns
      else:
          for i, col in enumerate(left.columns):
              assert col in right
              lcol = left.iloc[:, i]
              rcol = right.iloc[:, i]
              assert_series_equal(
                  lcol, rcol, check_dtype=check_dtype,
                  check_index_type=check_index_type,
                  check_less_precise=check_less_precise,
                  check_exact=check_exact, check_names=check_names,
                  check_datetimelike_compat=check_datetimelike_compat,
                  check_categorical=check_categorical,
                  obj='DataFrame.iloc[:, {idx}]'.format(idx=i))
  
  
  def assert_panel_equal(left, right,
                         check_dtype=True,
                         check_panel_type=False,
                         check_less_precise=False,
                         check_names=False,
                         by_blocks=False,
                         obj='Panel'):
      """Check that left and right Panels are equal.
  
      Parameters
      ----------
      left : Panel (or nd)
      right : Panel (or nd)
      check_dtype : bool, default True
          Whether to check the Panel dtype is identical.
      check_panel_type : bool, default False
          Whether to check the Panel class is identical.
      check_less_precise : bool or int, default False
          Specify comparison precision. Only used when check_exact is False.
          5 digits (False) or 3 digits (True) after decimal points are compared.
          If int, then specify the digits to compare
      check_names : bool, default True
          Whether to check the Index names attribute.
      by_blocks : bool, default False
          Specify how to compare internal data. If False, compare by columns.
          If True, compare by blocks.
      obj : str, default 'Panel'
          Specify the object name being compared, internally used to show
          the appropriate assertion message.
      """
  
      if check_panel_type:
          assert_class_equal(left, right, obj=obj)
  
      for axis in left._AXIS_ORDERS:
          left_ind = getattr(left, axis)
          right_ind = getattr(right, axis)
          assert_index_equal(left_ind, right_ind, check_names=check_names)
  
      if by_blocks:
          rblocks = right._to_dict_of_blocks()
          lblocks = left._to_dict_of_blocks()
          for dtype in list(set(list(lblocks.keys()) + list(rblocks.keys()))):
              assert dtype in lblocks
              assert dtype in rblocks
              array_equivalent(lblocks[dtype].values, rblocks[dtype].values)
      else:
  
          # can potentially be slow
          for i, item in enumerate(left._get_axis(0)):
              msg = "non-matching item (right) '{item}'".format(item=item)
              assert item in right, msg
              litem = left.iloc[i]
              ritem = right.iloc[i]
              assert_frame_equal(litem, ritem,
                                 check_less_precise=check_less_precise,
                                 check_names=check_names)
  
          for i, item in enumerate(right._get_axis(0)):
              msg = "non-matching item (left) '{item}'".format(item=item)
              assert item in left, msg
  
  
  # -----------------------------------------------------------------------------
  # Sparse
  
  
  def assert_sp_array_equal(left, right, check_dtype=True):
      """Check that the left and right SparseArray are equal.
  
      Parameters
      ----------
      left : SparseArray
      right : SparseArray
      check_dtype : bool, default True
          Whether to check the data dtype is identical.
      """
  
      _check_isinstance(left, right, pd.SparseArray)
  
      assert_numpy_array_equal(left.sp_values, right.sp_values,
                               check_dtype=check_dtype)
  
      # SparseIndex comparison
      assert isinstance(left.sp_index, pd._libs.sparse.SparseIndex)
      assert isinstance(right.sp_index, pd._libs.sparse.SparseIndex)
  
      if not left.sp_index.equals(right.sp_index):
          raise_assert_detail('SparseArray.index', 'index are not equal',
                              left.sp_index, right.sp_index)
  
      assert_attr_equal('fill_value', left, right)
      if check_dtype:
          assert_attr_equal('dtype', left, right)
      assert_numpy_array_equal(left.values, right.values,
                               check_dtype=check_dtype)
  
  
  def assert_sp_series_equal(left, right, check_dtype=True, exact_indices=True,
                             check_series_type=True, check_names=True,
                             obj='SparseSeries'):
      """Check that the left and right SparseSeries are equal.
  
      Parameters
      ----------
      left : SparseSeries
      right : SparseSeries
      check_dtype : bool, default True
          Whether to check the Series dtype is identical.
      exact_indices : bool, default True
      check_series_type : bool, default True
          Whether to check the SparseSeries class is identical.
      check_names : bool, default True
          Whether to check the SparseSeries name attribute.
      obj : str, default 'SparseSeries'
          Specify the object name being compared, internally used to show
          the appropriate assertion message.
      """
      _check_isinstance(left, right, pd.SparseSeries)
  
      if check_series_type:
          assert_class_equal(left, right, obj=obj)
  
      assert_index_equal(left.index, right.index,
                         obj='{obj}.index'.format(obj=obj))
  
      assert_sp_array_equal(left.block.values, right.block.values)
  
      if check_names:
          assert_attr_equal('name', left, right)
      if check_dtype:
          assert_attr_equal('dtype', left, right)
  
      assert_numpy_array_equal(left.values, right.values)
  
  
  def assert_sp_frame_equal(left, right, check_dtype=True, exact_indices=True,
                            check_frame_type=True, obj='SparseDataFrame'):
      """Check that the left and right SparseDataFrame are equal.
  
      Parameters
      ----------
      left : SparseDataFrame
      right : SparseDataFrame
      check_dtype : bool, default True
          Whether to check the Series dtype is identical.
      exact_indices : bool, default True
          SparseSeries SparseIndex objects must be exactly the same,
          otherwise just compare dense representations.
      check_frame_type : bool, default True
          Whether to check the SparseDataFrame class is identical.
      obj : str, default 'SparseDataFrame'
          Specify the object name being compared, internally used to show
          the appropriate assertion message.
      """
      _check_isinstance(left, right, pd.SparseDataFrame)
  
      if check_frame_type:
          assert_class_equal(left, right, obj=obj)
  
      assert_index_equal(left.index, right.index,
                         obj='{obj}.index'.format(obj=obj))
      assert_index_equal(left.columns, right.columns,
                         obj='{obj}.columns'.format(obj=obj))
  
      for col, series in compat.iteritems(left):
          assert (col in right)
          # trade-off?
  
          if exact_indices:
              assert_sp_series_equal(series, right[col],
                                     check_dtype=check_dtype)
          else:
              assert_series_equal(series.to_dense(), right[col].to_dense(),
                                  check_dtype=check_dtype)
  
      assert_attr_equal('default_fill_value', left, right, obj=obj)
  
      # do I care?
      # assert(left.default_kind == right.default_kind)
  
      for col in right:
          assert (col in left)
  
  # -----------------------------------------------------------------------------
  # Others
  
  
  def assert_contains_all(iterable, dic):
      for k in iterable:
          assert k in dic, "Did not contain item: '{key!r}'".format(key=k)
  
  
  def assert_copy(iter1, iter2, **eql_kwargs):
      """
      iter1, iter2: iterables that produce elements
      comparable with assert_almost_equal
  
      Checks that the elements are equal, but not
      the same object. (Does not check that items
      in sequences are also not the same object)
      """
      for elem1, elem2 in zip(iter1, iter2):
          assert_almost_equal(elem1, elem2, **eql_kwargs)
          msg = ("Expected object {obj1!r} and object {obj2!r} to be "
                 "different objects, but they were the same object."
                 ).format(obj1=type(elem1), obj2=type(elem2))
          assert elem1 is not elem2, msg
  
  
  def getCols(k):
      return string.ascii_uppercase[:k]
  
  
  def getArangeMat():
      return np.arange(N * K).reshape((N, 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(np.random.choice(x, k), 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 makeIntIndex(k=10, name=None):
      return Index(lrange(k), name=name)
  
  
  def makeUIntIndex(k=10, name=None):
      return Index([2**63 + i for i in lrange(k)], name=name)
  
  
  def makeRangeIndex(k=10, name=None, **kwargs):
      return RangeIndex(0, k, 1, name=name, **kwargs)
  
  
  def makeFloatIndex(k=10, name=None):
      values = sorted(np.random.random_sample(k)) - np.random.random_sample(1)
      return Index(values * (10 ** np.random.randint(0, 9)), name=name)
  
  
  def makeDateIndex(k=10, freq='B', name=None, **kwargs):
      dt = datetime(2000, 1, 1)
      dr = bdate_range(dt, periods=k, freq=freq, name=name)
      return DatetimeIndex(dr, name=name, **kwargs)
  
  
  def makeTimedeltaIndex(k=10, freq='D', name=None, **kwargs):
      return TimedeltaIndex(start='1 day', periods=k, freq=freq,
                            name=name, **kwargs)
  
  
  def makePeriodIndex(k=10, name=None, **kwargs):
      dt = datetime(2000, 1, 1)
      dr = PeriodIndex(start=dt, periods=k, freq='B', name=name, **kwargs)
      return dr
  
  
  def makeMultiIndex(k=10, names=None, **kwargs):
      return MultiIndex.from_product(
          (('foo', 'bar'), (1, 2)), names=names, **kwargs)
  
  
  def all_index_generator(k=10):
      """Generator which can be iterated over to get instances of all the various
      index classes.
  
      Parameters
      ----------
      k: length of each of the index instances
      """
      all_make_index_funcs = [makeIntIndex, makeFloatIndex, makeStringIndex,
                              makeUnicodeIndex, makeDateIndex, makePeriodIndex,
                              makeTimedeltaIndex, makeBoolIndex, makeRangeIndex,
                              makeIntervalIndex,
                              makeCategoricalIndex]
      for make_index_func in all_make_index_funcs:
          yield make_index_func(k=k)
  
  
  def index_subclass_makers_generator():
      make_index_funcs = [
          makeDateIndex, makePeriodIndex,
          makeTimedeltaIndex, makeRangeIndex,
          makeIntervalIndex, makeCategoricalIndex,
          makeMultiIndex
      ]
      for make_index_func in make_index_funcs:
          yield make_index_func
  
  
  def all_timeseries_index_generator(k=10):
      """Generator which can be iterated over to get instances of all the classes
      which represent time-seires.
  
      Parameters
      ----------
      k: length of each of the index instances
      """
      make_index_funcs = [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(randn(N), index=index, name=name)
  
  
  def makeStringSeries(name=None):
      index = makeStringIndex(N)
      return Series(randn(N), index=index, name=name)
  
  
  def makeObjectSeries(name=None):
      dateIndex = makeDateIndex(N)
      dateIndex = Index(dateIndex, dtype=object)
      index = makeStringIndex(N)
      return Series(dateIndex, index=index, name=name)
  
  
  def getSeriesData():
      index = makeStringIndex(N)
      return {c: Series(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(randn(nper), index=makeDateIndex(nper, freq=freq), name=name)
  
  
  def makePeriodSeries(nper=None, name=None):
      if nper is None:
          nper = N
      return Series(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():
      data = getSeriesData()
      return DataFrame(data)
  
  
  def getMixedTypeDict():
      index = Index(['a', 'b', 'c', 'd', 'e'])
  
      data = {
          'A': [0., 1., 2., 3., 4.],
          'B': [0., 1., 0., 1., 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 makePanel(nper=None):
      with warnings.catch_warnings(record=True):
          cols = ['Item' + c for c in string.ascii_uppercase[:K - 1]]
          data = {c: makeTimeDataFrame(nper) for c in cols}
          return Panel.fromDict(data)
  
  
  def makePeriodPanel(nper=None):
      with warnings.catch_warnings(record=True):
          cols = ['Item' + c for c in string.ascii_uppercase[:K - 1]]
          data = {c: makePeriodFrame(nper) for c in cols}
          return Panel.fromDict(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 singelton case uniform
      if isinstance(names, compat.string_types) and nlevels == 1:
          names = [names]
  
      # specific 1D index type requested?
      idx_func = dict(i=makeIntIndex, f=makeFloatIndex,
                      s=makeStringIndex, u=makeUnicodeIndex,
                      dt=makeDateIndex, td=makeTimedeltaIndex,
                      p=makePeriodIndex).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('"{idx_type}" is not a legal value for `idx_type`, '
                           'use  "i"/"f"/"s"/"u"/"dt/"p"/"td".'
                           .format(idx_type=idx_type))
  
      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)
  
      tuples = []
      for i in range(nlevels):
          def keyfunc(x):
              import re
              numeric_tuple = re.sub(r"[^\d_]_?", "", x).split("_")
              return lmap(int, numeric_tuple)
  
          # build a list of lists to create the index from
          div_factor = nentries // ndupe_l[i] + 1
          cnt = Counter()
          for j in range(div_factor):
              label = '{prefix}_l{i}_g{j}'.format(prefix=prefix, i=i, j=j)
              cnt[label] = ndupe_l[i]
          # cute Counter trick
          result = list(sorted(cnt.elements(), key=keyfunc))[:nentries]
          tuples.append(result)
  
      tuples = lzip(*tuples)
  
      # 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):
      """
     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 conjuncion 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","FI","FO","FAM"],
                               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: "R{rows}C{cols}".format(rows=r, cols=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 = int(np.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. / nrows).astype(int)
      i = (ind - j * nrows).astype(int)
      return i.tolist(), j.tolist()
  
  
  def makeMissingCustomDataframe(nrows, ncols, density=.9, random_state=None,
                                 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):
      """
      Parameters
      ----------
      Density : float, optional
          Float in (0, 1) that gives the percentage of non-missing numbers in
          the DataFrame.
      random_state : {np.random.RandomState, int}, optional
          Random number generator or random seed.
  
      See makeCustomDataframe for descriptions of the rest of the parameters.
      """
      df = makeCustomDataframe(nrows, ncols, c_idx_names=c_idx_names,
                               r_idx_names=r_idx_names,
                               c_idx_nlevels=c_idx_nlevels,
                               r_idx_nlevels=r_idx_nlevels,
                               data_gen_f=data_gen_f,
                               c_ndupe_l=c_ndupe_l, r_ndupe_l=r_ndupe_l,
                               dtype=dtype, c_idx_type=c_idx_type,
                               r_idx_type=r_idx_type)
  
      i, j = _create_missing_idx(nrows, ncols, density, random_state)
      df.values[i, j] = np.nan
      return df
  
  
  def makeMissingDataframe(density=.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 add_nans(panel):
      I, J, N = panel.shape
      for i, item in enumerate(panel.items):
          dm = panel[item]
          for j, col in enumerate(dm.columns):
              dm[col][:i + j] = np.NaN
      return panel
  
  
  def add_nans_panel4d(panel4d):
      for l, label in enumerate(panel4d.labels):
          panel = panel4d[label]
          add_nans(panel)
      return panel4d
  
  
  class TestSubDict(dict):
  
      def __init__(self, *args, **kwargs):
          dict.__init__(self, *args, **kwargs)
  
  
  def optional_args(decorator):
      """allows a decorator to take optional positional and keyword arguments.
      Assumes that taking a single, callable, positional argument means that
      it is decorating a function, i.e. something like this::
  
          @my_decorator
          def function(): pass
  
      Calls decorator with decorator(f, *args, **kwargs)"""
  
      @wraps(decorator)
      def wrapper(*args, **kwargs):
          def dec(f):
              return decorator(f, *args, **kwargs)
  
          is_decorating = not kwargs and len(args) == 1 and callable(args[0])
          if is_decorating:
              f = args[0]
              args = []
              return dec(f)
          else:
              return dec
  
      return wrapper
  
  
  # skip tests on exceptions with this message
  _network_error_messages = (
      # 'urlopen error timed out',
      # 'timeout: timed out',
      # 'socket.timeout: timed out',
      'timed out',
      'Server Hangup',
      'HTTP Error 503: Service Unavailable',
      '502: Proxy Error',
      'HTTP Error 502: internal error',
      'HTTP Error 502',
      'HTTP Error 503',
      'HTTP Error 403',
      'HTTP Error 400',
      'Temporary failure in name resolution',
      'Name or service not known',
      'Connection refused',
      'certificate verify',
  )
  
  # or this e.errno/e.reason.errno
  _network_errno_vals = (
      101,  # Network is unreachable
      111,  # Connection refused
      110,  # Connection timed out
      104,  # Connection reset Error
      54,   # Connection reset by peer
      60,   # urllib.error.URLError: [Errno 60] Connection timed out
  )
  
  # Both of the above shouldn't mask real issues such as 404's
  # or refused connections (changed DNS).
  # But some tests (test_data yahoo) contact incredibly flakey
  # servers.
  
  # and conditionally raise on these exception types
  _network_error_classes = (IOError, httplib.HTTPException)
  
  if sys.version_info >= (3, 3):
      _network_error_classes += (TimeoutError,)  # noqa
  
  
  def can_connect(url, error_classes=_network_error_classes):
      """Try to connect to the given url. True if succeeds, False if IOError
      raised
  
      Parameters
      ----------
      url : basestring
          The URL to try to connect to
  
      Returns
      -------
      connectable : bool
          Return True if no IOError (unable to connect) or URLError (bad url) was
          raised
      """
      try:
          with urlopen(url):
              pass
      except error_classes:
          return False
      else:
          return True
  
  
  @optional_args
  def network(t, url="http://www.google.com",
              raise_on_error=_RAISE_NETWORK_ERROR_DEFAULT,
              check_before_test=False,
              error_classes=_network_error_classes,
              skip_errnos=_network_errno_vals,
              _skip_on_messages=_network_error_messages,
              ):
      """
      Label a test as requiring network connection and, if an error is
      encountered, only raise if it does not find a network connection.
  
      In comparison to ``network``, this assumes an added contract to your test:
      you must assert that, under normal conditions, your test will ONLY fail if
      it does not have network connectivity.
  
      You can call this in 3 ways: as a standard decorator, with keyword
      arguments, or with a positional argument that is the url to check.
  
      Parameters
      ----------
      t : callable
          The test requiring network connectivity.
      url : path
          The url to test via ``pandas.io.common.urlopen`` to check
          for connectivity. Defaults to 'http://www.google.com'.
      raise_on_error : bool
          If True, never catches errors.
      check_before_test : bool
          If True, checks connectivity before running the test case.
      error_classes : tuple or Exception
          error classes to ignore. If not in ``error_classes``, raises the error.
          defaults to IOError. Be careful about changing the error classes here.
      skip_errnos : iterable of int
          Any exception that has .errno or .reason.erno set to one
          of these values will be skipped with an appropriate
          message.
      _skip_on_messages: iterable of string
          any exception e for which one of the strings is
          a substring of str(e) will be skipped with an appropriate
          message. Intended to suppress errors where an errno isn't available.
  
      Notes
      -----
      * ``raise_on_error`` supercedes ``check_before_test``
  
      Returns
      -------
      t : callable
          The decorated test ``t``, with checks for connectivity errors.
  
      Example
      -------
  
      Tests decorated with @network will fail if it's possible to make a network
      connection to another URL (defaults to google.com)::
  
        >>> from pandas.util.testing import network
        >>> from pandas.io.common import urlopen
        >>> @network
        ... def test_network():
        ...     with urlopen("rabbit://bonanza.com"):
        ...         pass
        Traceback
           ...
        URLError: <urlopen error unknown url type: rabit>
  
        You can specify alternative URLs::
  
          >>> @network("http://www.yahoo.com")
          ... def test_something_with_yahoo():
          ...    raise IOError("Failure Message")
          >>> test_something_with_yahoo()
          Traceback (most recent call last):
              ...
          IOError: Failure Message
  
      If you set check_before_test, it will check the url first and not run the
      test on failure::
  
          >>> @network("failing://url.blaher", check_before_test=True)
          ... def test_something():
          ...     print("I ran!")
          ...     raise ValueError("Failure")
          >>> test_something()
          Traceback (most recent call last):
              ...
  
      Errors not related to networking will always be raised.
      """
      from pytest import skip
      t.network = True
  
      @compat.wraps(t)
      def wrapper(*args, **kwargs):
          if check_before_test and not raise_on_error:
              if not can_connect(url, error_classes):
                  skip()
          try:
              return t(*args, **kwargs)
          except Exception as e:
              errno = getattr(e, 'errno', None)
              if not errno and hasattr(errno, "reason"):
                  errno = getattr(e.reason, 'errno', None)
  
              if errno in skip_errnos:
                  skip("Skipping test due to known errno"
                       " and error {error}".format(error=e))
  
              try:
                  e_str = traceback.format_exc(e)
              except Exception:
                  e_str = str(e)
  
              if any(m.lower() in e_str.lower() for m in _skip_on_messages):
                  skip("Skipping test because exception "
                       "message is known and error {error}".format(error=e))
  
              if not isinstance(e, error_classes):
                  raise
  
              if raise_on_error or can_connect(url, error_classes):
                  raise
              else:
                  skip("Skipping test due to lack of connectivity"
                       " and error {error}".format(e))
  
      return wrapper
  
  
  with_connectivity_check = network
  
  
  class SimpleMock(object):
  
      """
      Poor man's mocking object
  
      Note: only works for new-style classes, assumes  __getattribute__ exists.
  
      >>> a = type("Duck",(),{})
      >>> a.attr1,a.attr2 ="fizz","buzz"
      >>> b = SimpleMock(a,"attr1","bar")
      >>> b.attr1 == "bar" and b.attr2 == "buzz"
      True
      >>> a.attr1 == "fizz" and a.attr2 == "buzz"
      True
      """
  
      def __init__(self, obj, *args, **kwds):
          assert(len(args) % 2 == 0)
          attrs = kwds.get("attrs", {})
          for k, v in zip(args[::2], args[1::2]):
              # dict comprehensions break 2.6
              attrs[k] = v
          self.attrs = attrs
          self.obj = obj
  
      def __getattribute__(self, name):
          attrs = object.__getattribute__(self, "attrs")
          obj = object.__getattribute__(self, "obj")
          return attrs.get(name, type(obj).__getattribute__(obj, name))
  
  
  @contextmanager
  def stdin_encoding(encoding=None):
      """
      Context manager for running bits of code while emulating an arbitrary
      stdin encoding.
  
      >>> import sys
      >>> _encoding = sys.stdin.encoding
      >>> with stdin_encoding('AES'): sys.stdin.encoding
      'AES'
      >>> sys.stdin.encoding==_encoding
      True
  
      """
      import sys
  
      _stdin = sys.stdin
      sys.stdin = SimpleMock(sys.stdin, "encoding", encoding)
      yield
      sys.stdin = _stdin
  
  
  def assert_raises_regex(_exception, _regexp, _callable=None,
                          *args, **kwargs):
      r"""
      Check that the specified Exception is raised and that the error message
      matches a given regular expression pattern. This may be a regular
      expression object or a string containing a regular expression suitable
      for use by `re.search()`. This is a port of the `assertRaisesRegexp`
      function from unittest in Python 2.7.
  
      Examples
      --------
      >>> assert_raises_regex(ValueError, 'invalid literal for.*XYZ', int, 'XYZ')
      >>> import re
      >>> assert_raises_regex(ValueError, re.compile('literal'), int, 'XYZ')
  
      If an exception of a different type is raised, it bubbles up.
  
      >>> assert_raises_regex(TypeError, 'literal', int, 'XYZ')
      Traceback (most recent call last):
          ...
      ValueError: invalid literal for int() with base 10: 'XYZ'
      >>> dct = dict()
      >>> assert_raises_regex(KeyError, 'pear', dct.__getitem__, 'apple')
      Traceback (most recent call last):
          ...
      AssertionError: "pear" does not match "'apple'"
  
      You can also use this in a with statement.
      >>> with assert_raises_regex(TypeError, 'unsupported operand type\(s\)'):
      ...     1 + {}
      >>> with assert_raises_regex(TypeError, 'banana'):
      ...     'apple'[0] = 'b'
      Traceback (most recent call last):
          ...
      AssertionError: "banana" does not match "'str' object does not support \
  item assignment"
      """
      manager = _AssertRaisesContextmanager(exception=_exception, regexp=_regexp)
      if _callable is not None:
          with manager:
              _callable(*args, **kwargs)
      else:
          return manager
  
  
  class _AssertRaisesContextmanager(object):
      """
      Context manager behind `assert_raises_regex`.
      """
  
      def __init__(self, exception, regexp=None):
          """
          Initialize an _AssertRaisesContextManager instance.
  
          Parameters
          ----------
          exception : class
              The expected Exception class.
          regexp : str, default None
              The regex to compare against the Exception message.
          """
  
          self.exception = exception
  
          if regexp is not None and not hasattr(regexp, "search"):
              regexp = re.compile(regexp, re.DOTALL)
  
          self.regexp = regexp
  
      def __enter__(self):
          return self
  
      def __exit__(self, exc_type, exc_value, trace_back):
          expected = self.exception
  
          if not exc_type:
              exp_name = getattr(expected, "__name__", str(expected))
              raise AssertionError("{name} not raised.".format(name=exp_name))
  
          return self.exception_matches(exc_type, exc_value, trace_back)
  
      def exception_matches(self, exc_type, exc_value, trace_back):
          """
          Check that the Exception raised matches the expected Exception
          and expected error message regular expression.
  
          Parameters
          ----------
          exc_type : class
              The type of Exception raised.
          exc_value : Exception
              The instance of `exc_type` raised.
          trace_back : stack trace object
              The traceback object associated with `exc_value`.
  
          Returns
          -------
          is_matched : bool
              Whether or not the Exception raised matches the expected
              Exception class and expected error message regular expression.
  
          Raises
          ------
          AssertionError : The error message provided does not match
                           the expected error message regular expression.
          """
  
          if issubclass(exc_type, self.exception):
              if self.regexp is not None:
                  val = str(exc_value)
  
                  if not self.regexp.search(val):
                      msg = '"{pat}" does not match "{val}"'.format(
                          pat=self.regexp.pattern, val=val)
                      e = AssertionError(msg)
                      raise_with_traceback(e, trace_back)
  
              return True
          else:
              # Failed, so allow Exception to bubble up.
              return False
  
  
  @contextmanager
  def assert_produces_warning(expected_warning=Warning, filter_level="always",
                              clear=None, check_stacklevel=True):
      """
      Context manager for running code expected to either raise a specific
      warning, or not raise any warnings. Verifies that the code raises the
      expected warning, and that it does not raise any other unexpected
      warnings. It is basically a wrapper around ``warnings.catch_warnings``.
  
      Parameters
      ----------
      expected_warning : {Warning, False, None}, default Warning
          The type of Exception raised. ``exception.Warning`` is the base
          class for all warnings. To check that no warning is returned,
          specify ``False`` or ``None``.
      filter_level : str, default "always"
          Specifies whether warnings are ignored, displayed, or turned
          into errors.
          Valid values are:
  
          * "error" - turns matching warnings into exceptions
          * "ignore" - discard the warning
          * "always" - always emit a warning
          * "default" - print the warning the first time it is generated
            from each location
          * "module" - print the warning the first time it is generated
            from each module
          * "once" - print the warning the first time it is generated
  
      clear : str, default None
          If not ``None`` then remove any previously raised warnings from
          the ``__warningsregistry__`` to ensure that no warning messages are
          suppressed by this context manager. If ``None`` is specified,
          the ``__warningsregistry__`` keeps track of which warnings have been
           shown, and does not show them again.
      check_stacklevel : bool, default True
          If True, displays the line that called the function containing
          the warning to show were the function is called. Otherwise, the
          line that implements the function is displayed.
  
      Examples
      --------
      >>> import warnings
      >>> with assert_produces_warning():
      ...     warnings.warn(UserWarning())
      ...
      >>> with assert_produces_warning(False):
      ...     warnings.warn(RuntimeWarning())
      ...
      Traceback (most recent call last):
          ...
      AssertionError: Caused unexpected warning(s): ['RuntimeWarning'].
      >>> with assert_produces_warning(UserWarning):
      ...     warnings.warn(RuntimeWarning())
      Traceback (most recent call last):
          ...
      AssertionError: Did not see expected warning of class 'UserWarning'.
  
      ..warn:: This is *not* thread-safe.
      """
      with warnings.catch_warnings(record=True) as w:
  
          if clear is not None:
              # make sure that we are clearning these warnings
              # if they have happened before
              # to guarantee that we will catch them
              if not is_list_like(clear):
                  clear = [clear]
              for m in clear:
                  try:
                      m.__warningregistry__.clear()
                  except Exception:
                      pass
  
          saw_warning = False
          warnings.simplefilter(filter_level)
          yield w
          extra_warnings = []
  
          for actual_warning in w:
              if (expected_warning and issubclass(actual_warning.category,
                                                  expected_warning)):
                  saw_warning = True
  
                  if check_stacklevel and issubclass(actual_warning.category,
                                                     (FutureWarning,
                                                      DeprecationWarning)):
                      from inspect import getframeinfo, stack
                      caller = getframeinfo(stack()[2][0])
                      msg = ("Warning not set with correct stacklevel. "
                             "File where warning is raised: {actual} != "
                             "{caller}. Warning message: {message}"
                             ).format(actual=actual_warning.filename,
                                      caller=caller.filename,
                                      message=actual_warning.message)
                      assert actual_warning.filename == caller.filename, msg
              else:
                  extra_warnings.append(actual_warning.category.__name__)
          if expected_warning:
              msg = "Did not see expected warning of class {name!r}.".format(
                  name=expected_warning.__name__)
              assert saw_warning, msg
          assert not extra_warnings, ("Caused unexpected warning(s): {extra!r}."
                                      ).format(extra=extra_warnings)
  
  
  class RNGContext(object):
      """
      Context manager to set the numpy random number generator speed. Returns
      to the original value upon exiting the context manager.
  
      Parameters
      ----------
      seed : int
          Seed for numpy.random.seed
  
      Examples
      --------
  
      with RNGContext(42):
          np.random.randn()
      """
  
      def __init__(self, seed):
          self.seed = seed
  
      def __enter__(self):
  
          self.start_state = np.random.get_state()
          np.random.seed(self.seed)
  
      def __exit__(self, exc_type, exc_value, traceback):
  
          np.random.set_state(self.start_state)
  
  
  @contextmanager
  def use_numexpr(use, min_elements=None):
      from pandas.core.computation import expressions as expr
      if min_elements is None:
          min_elements = expr._MIN_ELEMENTS
  
      olduse = expr._USE_NUMEXPR
      oldmin = expr._MIN_ELEMENTS
      expr.set_use_numexpr(use)
      expr._MIN_ELEMENTS = min_elements
      yield
      expr._MIN_ELEMENTS = oldmin
      expr.set_use_numexpr(olduse)
  
  
  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 SubclassedSparseSeries(pd.SparseSeries):
      _metadata = ['testattr']
  
      @property
      def _constructor(self):
          return SubclassedSparseSeries
  
      @property
      def _constructor_expanddim(self):
          return SubclassedSparseDataFrame
  
  
  class SubclassedSparseDataFrame(pd.SparseDataFrame):
      _metadata = ['testattr']
  
      @property
      def _constructor(self):
          return SubclassedSparseDataFrame
  
      @property
      def _constructor_sliced(self):
          return SubclassedSparseSeries
  
  
  class SubclassedCategorical(Categorical):
  
      @property
      def _constructor(self):
          return SubclassedCategorical
  
  
  @contextmanager
  def patch(ob, attr, value):
      """Temporarily patch an attribute of an object.
  
      Parameters
      ----------
      ob : any
          The object to patch. This must support attribute assignment for `attr`.
      attr : str
          The name of the attribute to patch.
      value : any
          The temporary attribute to assign.
  
      Examples
      --------
      >>> class C(object):
      ...     attribute = 'original'
      ...
      >>> C.attribute
      'original'
      >>> with patch(C, 'attribute', 'patched'):
      ...     in_context = C.attribute
      ...
      >>> in_context
      'patched'
      >>> C.attribute  # the value is reset when the context manager exists
      'original'
  
      Correctly replaces attribute when the manager exits with an exception.
      >>> with patch(C, 'attribute', 'patched'):
      ...     in_context = C.attribute
      ...     raise ValueError()
      Traceback (most recent call last):
         ...
      ValueError
      >>> in_context
      'patched'
      >>> C.attribute
      'original'
      """
      noattr = object()  # mark that the attribute never existed
      old = getattr(ob, attr, noattr)
      setattr(ob, attr, value)
      try:
          yield
      finally:
          if old is noattr:
              delattr(ob, attr)
          else:
              setattr(ob, attr, old)
  
  
  @contextmanager
  def set_timezone(tz):
      """Context manager for temporarily setting a timezone.
  
      Parameters
      ----------
      tz : str
          A string representing a valid timezone.
  
      Examples
      --------
  
      >>> from datetime import datetime
      >>> from dateutil.tz import tzlocal
      >>> tzlocal().tzname(datetime.now())
      'IST'
  
      >>> with set_timezone('US/Eastern'):
      ...     tzlocal().tzname(datetime.now())
      ...
      'EDT'
      """
  
      import os
      import time
  
      def setTZ(tz):
          if tz is None:
              try:
                  del os.environ['TZ']
              except KeyError:
                  pass
          else:
              os.environ['TZ'] = tz
              time.tzset()
  
      orig_tz = os.environ.get('TZ')
      setTZ(tz)
      try:
          yield
      finally:
          setTZ(orig_tz)
  
  
  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
      -------
      skipna_wrapper : 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
  
apis: pandas.compat.iteritems pandas.Series pandas.core.dtypes.common.is_number pandas.IntervalIndex pandas._libs.testing.assert_almost_equal pandas.bdate_range pandas.compat.map numpy.random.seed pandas.io.common.urlopen matplotlib.pyplot.get_fignums numpy.random.RandomState pandas.compat.zip pandas.RangeIndex pandas.reset_option pandas.core.common._all_not_none pandas.core.dtypes.common.is_list_like pandas.PeriodIndex pandas.core.dtypes.common.is_categorical_dtype pandas.compat.wraps pandas.compat.callable numpy.random.choice pandas.io.formats.printing.pprint_thing pandas.core.algorithms.take_1d pandas.compat.StringIO numpy.random.rand numpy.isnan pandas.core.computation.expressions.set_use_numexpr pandas.core.dtypes.common.is_datetimelike_v_object pandas.core.dtypes.common.needs_i8_conversion numpy.linspace pandas.compat.import_lzma pandas.compat.lzip pandas.compat.raise_with_traceback numpy.random.set_state pandas.read_pickle pandas.compat.Counter pandas.core.dtypes.common.is_datetimelike_v_numeric numpy.random.get_state pandas._libs.testing.assert_dict_equal pandas.MultiIndex.from_product pandas.IntervalIndex.from_breaks pandas.core.dtypes.common.is_interval_dtype pandas.TimedeltaIndex numpy.arange pandas.MultiIndex.from_tuples matplotlib.pyplot.close pandas.to_pickle numpy.prod pandas.Index numpy.array pandas.core.dtypes.common.is_bool pandas.compat.u numpy.random.random_sample pandas.DatetimeIndex pandas.Panel.fromDict pandas.DataFrame numpy.random.randn pandas.compat.lmap numpy.floor pandas.compat.range pandas.compat.lrange numpy.round pandas.core.dtypes.common.is_sequence numpy.random.randint pandas.core.dtypes.missing.array_equivalent