# Buggy code
def form_blocks(data, axes):
    # pre-filter out items if we passed it
    items = axes[0]

    if len(data) < len(items):
        extra_items = items - Index(data.keys())
    else:
        extra_items = []

    # put "leftover" items in float bucket, where else?
    # generalize?
    float_dict = {}
    int_dict = {}
    bool_dict = {}
    object_dict = {}
    for k, v in data.iteritems():
        if issubclass(v.dtype.type, np.floating):
            float_dict[k] = v
        elif issubclass(v.dtype.type, np.integer):
            int_dict[k] = v
        elif v.dtype == np.bool_:
            bool_dict[k] = v
        else:
            object_dict[k] = v

    blocks = []
    if len(float_dict):
        float_block = _simple_blockify(float_dict, items, np.float64)
        blocks.append(float_block)

    if len(int_dict):
        int_block = _simple_blockify(int_dict, items, np.int64)
        blocks.append(int_block)

    if len(bool_dict):
        bool_block = _simple_blockify(bool_dict, items, np.bool_)
        blocks.append(bool_block)

    if len(object_dict) > 0:
        object_block = _simple_blockify(object_dict, items, np.object_)
        blocks.append(object_block)

    if len(extra_items):
        shape = (len(extra_items),) + tuple(len(x) for x in axes[1:])
        block_values = np.empty(shape, dtype=float)
        block_values.fill(nan)

        na_block = make_block(block_values, extra_items, items, do_integrity_check=True)
        blocks.append(na_block)
        blocks = _consolidate(blocks, items)

    return blocks


# Traceback (ValueError)
In [17]: date_range(datetime.datetime.today(), periods=10, freq='2h20m')
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/home/chang/Dropbox/git/pandas/<ipython-input-17-ff4e03382573> in <module>()
----> 1 date_range(datetime.datetime.today(), periods=10, freq='2h20m')

/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize)
1209     """
1210     return DatetimeIndex(start=start, end=end, periods=periods,
-> 1211                          freq=freq, tz=tz, normalize=normalize)
1212
1213

/home/chang/Dropbox/git/pandas/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, **kwds)
202
203         if data is None and offset is None:
--> 204             raise ValueError("Must provide freq argument if no data is "
205                              "supplied")
206

ValueError: Must provide freq argument if no data is supplied

# Fixed code
def form_blocks(data, axes):
    # pre-filter out items if we passed it
    items = axes[0]

    if len(data) < len(items):
        extra_items = items - Index(data.keys())
    else:
        extra_items = []

    # put "leftover" items in float bucket, where else?
    # generalize?
    float_dict = {}
    complex_dict = {}
    int_dict = {}
    bool_dict = {}
    object_dict = {}
    datetime_dict = {}
    for k, v in data.iteritems():
        if issubclass(v.dtype.type, np.floating):
            float_dict[k] = v
        elif issubclass(v.dtype.type, np.complexfloating):
            complex_dict[k] = v
        elif issubclass(v.dtype.type, np.datetime64):
            datetime_dict[k] = v
        elif issubclass(v.dtype.type, np.integer):
            int_dict[k] = v
        elif v.dtype == np.bool_:
            bool_dict[k] = v
        else:
            object_dict[k] = v

    blocks = []
    if len(float_dict):
        float_block = _simple_blockify(float_dict, items, np.float64)
        blocks.append(float_block)

    if len(complex_dict):
        complex_block = _simple_blockify(complex_dict, items, np.complex128)
        blocks.append(complex_block)

    if len(int_dict):
        int_block = _simple_blockify(int_dict, items, np.int64)
        blocks.append(int_block)

    if len(datetime_dict):
        datetime_block = _simple_blockify(datetime_dict, items, np.dtype("M8[ns]"))
        blocks.append(datetime_block)

    if len(bool_dict):
        bool_block = _simple_blockify(bool_dict, items, np.bool_)
        blocks.append(bool_block)

    if len(object_dict) > 0:
        object_block = _simple_blockify(object_dict, items, np.object_)
        blocks.append(object_block)

    if len(extra_items):
        shape = (len(extra_items),) + tuple(len(x) for x in axes[1:])
        block_values = np.empty(shape, dtype=float)
        block_values.fill(nan)

        na_block = make_block(block_values, extra_items, items, do_integrity_check=True)
        blocks.append(na_block)
        blocks = _consolidate(blocks, items)

    return blocks