old_contents: """
  basic inference routines
  
  most functions taken from pandas (https://github.com/pandas-dev/pandas)
  License BSD
  
  """
  
  import collections
  import re
  import numpy as np
  from numbers import Number
  from six import string_types, binary_type, text_type
  
  string_and_binary_types = (string_types,) + (binary_type,)
  
  
  def is_number(obj):
      return isinstance(obj, (Number, np.number))
  
  
  def is_string_like(obj):
      return isinstance(obj, (text_type, string_types))
  
  
  def _iterable_not_string(x):
      return (isinstance(x, collections.Iterable) and
              not isinstance(x, string_types))
  
  
  def is_iterator(obj):
      # python 3 generators have __next__ instead of next
      return hasattr(obj, 'next') or hasattr(obj, '__next__')
  
  
  def is_re(obj):
      return isinstance(obj, re._pattern_type)
  
  
  def is_re_compilable(obj):
      try:
          re.compile(obj)
      except TypeError:
          return False
      else:
          return True
  
  
  def is_list_like(arg):
      return (hasattr(arg, '__iter__') and
              not isinstance(arg, string_and_binary_types))
  
  
  def is_dict_like(arg):
      return hasattr(arg, '__getitem__') and hasattr(arg, 'keys')
  
  
  def is_named_tuple(arg):
      return isinstance(arg, tuple) and hasattr(arg, '_fields')
  
  
  def is_hashable(arg):
      """Return True if hash(arg) will succeed, False otherwise.
  
      Some types will pass a test against collections.Hashable but fail when they
      are actually hashed with hash().
  
      Distinguish between these and other types by trying the call to hash() and
      seeing if they raise TypeError.
  
      Examples
      --------
      >>> a = ([],)
      >>> isinstance(a, collections.Hashable)
      True
      >>> is_hashable(a)
      False
      """
      # unfortunately, we can't use isinstance(arg, collections.Hashable), which
      # can be faster than calling hash, because numpy scalars on Python 3 fail
      # this test
  
      # reconsider this decision once this numpy bug is fixed:
      # https://github.com/numpy/numpy/issues/5562
  
      try:
          hash(arg)
      except TypeError:
          return False
      else:
          return True
  
  
  def is_sequence(x):
      try:
          iter(x)
          len(x)  # it has a length
          return not isinstance(x, string_and_binary_types)
      except (TypeError, AttributeError):
          return False
  
new_contents: """
  basic inference routines
  
  most functions taken from pandas (https://github.com/pandas-dev/pandas)
  License BSD
  
  """
  
  import collections
  import re
  from numbers import Number
  
  import numpy
  import pandas
  from six import string_types, binary_type, text_type
  
  string_and_binary_types = (string_types,) + (binary_type,)
  
  
  def is_number(obj):
      return isinstance(obj, (Number, numpy.number))
  
  
  def is_string_like(obj):
      return isinstance(obj, (text_type, string_types))
  
  
  def _iterable_not_string(x):
      return (isinstance(x, collections.Iterable) and
              not isinstance(x, string_types))
  
  
  def is_iterator(obj):
      # python 3 generators have __next__ instead of next
      return hasattr(obj, 'next') or hasattr(obj, '__next__')
  
  
  def is_re(obj):
      return isinstance(obj, re._pattern_type)
  
  
  def is_re_compilable(obj):
      try:
          re.compile(obj)
      except TypeError:
          return False
      else:
          return True
  
  
  def is_list_like(arg):
      return (hasattr(arg, '__iter__') and
              not isinstance(arg, string_and_binary_types))
  
  
  def is_dict_like(arg):
      return hasattr(arg, '__getitem__') and hasattr(arg, 'keys')
  
  
  def is_named_tuple(arg):
      return isinstance(arg, tuple) and hasattr(arg, '_fields')
  
  
  def is_hashable(arg):
      """Return True if hash(arg) will succeed, False otherwise.
  
      Some types will pass a test against collections.Hashable but fail when they
      are actually hashed with hash().
  
      Distinguish between these and other types by trying the call to hash() and
      seeing if they raise TypeError.
  
      Examples
      --------
      >>> a = ([],)
      >>> isinstance(a, collections.Hashable)
      True
      >>> is_hashable(a)
      False
      """
      # unfortunately, we can't use isinstance(arg, collections.Hashable), which
      # can be faster than calling hash, because numpy scalars on Python 3 fail
      # this test
  
      # reconsider this decision once this numpy bug is fixed:
      # https://github.com/numpy/numpy/issues/5562
  
      try:
          hash(arg)
      except TypeError:
          return False
      else:
          return True
  
  
  def is_sequence(x):
      try:
          iter(x)
          len(x)  # it has a length
          return not isinstance(x, string_and_binary_types)
      except (TypeError, AttributeError):
          return False
  
  
  def is_pandas_like(x):
  
      return isinstance(x, (pandas.Series, pandas.DataFrame))
  
  
  def is_numpy_like(x):
  
      return isinstance(x, (numpy.ndarray))
  
subject: Add types check for pandas and numpy