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 nlevels <= len(ndupe_l))
    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('"%s" is not a legal value for `idx_type`, use  '
                         '"i"/"f"/"s"/"u"/"dt/"p"/"td".' % 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("[^\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%d_g' % i + str(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:
        index = Index(tuples[0], name=names[0])
    else:
        index = MultiIndex.from_tuples(tuples, names=names)
    return index
dataset/ETHPy150Open pydata/pandas/pandas/util/testing.py/makeCustomIndex/swapped operands of `<=`