# Buggy code
def _subplots(
    nrows=1,
    ncols=1,
    sharex=False,
    sharey=False,
    squeeze=True,
    subplot_kw=None,
    ax=None,
    **fig_kw,
):
    """Create a figure with a set of subplots already made.

    This utility wrapper makes it convenient to create common layouts of
    subplots, including the enclosing figure object, in a single call.

    Keyword arguments:

    nrows : int
      Number of rows of the subplot grid.  Defaults to 1.

    ncols : int
      Number of columns of the subplot grid.  Defaults to 1.

    sharex : bool
      If True, the X axis will be shared amongst all subplots.

    sharex : bool
      If True, the Y axis will be shared amongst all subplots.

    squeeze : bool

      If True, extra dimensions are squeezed out from the returned axis object:
        - if only one subplot is constructed (nrows=ncols=1), the resulting
        single Axis object is returned as a scalar.
        - for Nx1 or 1xN subplots, the returned object is a 1-d numpy object
        array of Axis objects are returned as numpy 1-d arrays.
        - for NxM subplots with N>1 and M>1 are returned as a 2d array.

      If False, no squeezing at all is done: the returned axis object is always
      a 2-d array contaning Axis instances, even if it ends up being 1x1.

    subplot_kw : dict
      Dict with keywords passed to the add_subplot() call used to create each
      subplots.

    fig_kw : dict
      Dict with keywords passed to the figure() call.  Note that all keywords
      not recognized above will be automatically included here.

    ax : Matplotlib axis object, default None

    Returns:

    fig, ax : tuple
      - fig is the Matplotlib Figure object
      - ax can be either a single axis object or an array of axis objects if
      more than one supblot was created.  The dimensions of the resulting array
      can be controlled with the squeeze keyword, see above.

    **Examples:**

    x = np.linspace(0, 2*np.pi, 400)
    y = np.sin(x**2)

    # Just a figure and one subplot
    f, ax = plt.subplots()
    ax.plot(x, y)
    ax.set_title('Simple plot')

    # Two subplots, unpack the output array immediately
    f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
    ax1.plot(x, y)
    ax1.set_title('Sharing Y axis')
    ax2.scatter(x, y)

    # Four polar axes
    plt.subplots(2, 2, subplot_kw=dict(polar=True))
    """
    import matplotlib.pyplot as plt

    if subplot_kw is None:
        subplot_kw = {}

    if ax is None:
        fig = plt.figure(**fig_kw)
    else:
        fig = ax.get_figure()

    # Create empty object array to hold all axes.  It's easiest to make it 1-d
    # so we can just append subplots upon creation, and then
    nplots = nrows * ncols
    axarr = np.empty(nplots, dtype=object)

    # Create first subplot separately, so we can share it if requested
    ax0 = fig.add_subplot(nrows, ncols, 1, **subplot_kw)
    if sharex:
        subplot_kw["sharex"] = ax0
    if sharey:
        subplot_kw["sharey"] = ax0
    axarr[0] = ax0

    # Note off-by-one counting because add_subplot uses the MATLAB 1-based
    # convention.
    for i in range(1, nplots):
        axarr[i] = fig.add_subplot(nrows, ncols, i + 1, **subplot_kw)

    if squeeze:
        # Reshape the array to have the final desired dimension (nrow,ncol),
        # though discarding unneeded dimensions that equal 1.  If we only have
        # one subplot, just return it instead of a 1-element array.
        if nplots == 1:
            return fig, axarr[0]
        else:
            return fig, axarr.reshape(nrows, ncols).squeeze()
    else:
        # returned axis array will be always 2-d, even if nrows=ncols=1
        return fig, axarr.reshape(nrows, ncols)


# 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 _subplots(
    nrows=1,
    ncols=1,
    sharex=False,
    sharey=False,
    squeeze=True,
    subplot_kw=None,
    ax=None,
    **fig_kw,
):
    """Create a figure with a set of subplots already made.

    This utility wrapper makes it convenient to create common layouts of
    subplots, including the enclosing figure object, in a single call.

    Keyword arguments:

    nrows : int
      Number of rows of the subplot grid.  Defaults to 1.

    ncols : int
      Number of columns of the subplot grid.  Defaults to 1.

    sharex : bool
      If True, the X axis will be shared amongst all subplots.

    sharex : bool
      If True, the Y axis will be shared amongst all subplots.

    squeeze : bool

      If True, extra dimensions are squeezed out from the returned axis object:
        - if only one subplot is constructed (nrows=ncols=1), the resulting
        single Axis object is returned as a scalar.
        - for Nx1 or 1xN subplots, the returned object is a 1-d numpy object
        array of Axis objects are returned as numpy 1-d arrays.
        - for NxM subplots with N>1 and M>1 are returned as a 2d array.

      If False, no squeezing at all is done: the returned axis object is always
      a 2-d array contaning Axis instances, even if it ends up being 1x1.

    subplot_kw : dict
      Dict with keywords passed to the add_subplot() call used to create each
      subplots.

    fig_kw : dict
      Dict with keywords passed to the figure() call.  Note that all keywords
      not recognized above will be automatically included here.

    ax : Matplotlib axis object, default None

    Returns:

    fig, ax : tuple
      - fig is the Matplotlib Figure object
      - ax can be either a single axis object or an array of axis objects if
      more than one supblot was created.  The dimensions of the resulting array
      can be controlled with the squeeze keyword, see above.

    **Examples:**

    x = np.linspace(0, 2*np.pi, 400)
    y = np.sin(x**2)

    # Just a figure and one subplot
    f, ax = plt.subplots()
    ax.plot(x, y)
    ax.set_title('Simple plot')

    # Two subplots, unpack the output array immediately
    f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
    ax1.plot(x, y)
    ax1.set_title('Sharing Y axis')
    ax2.scatter(x, y)

    # Four polar axes
    plt.subplots(2, 2, subplot_kw=dict(polar=True))
    """
    import matplotlib.pyplot as plt

    if subplot_kw is None:
        subplot_kw = {}

    if ax is None:
        fig = plt.figure(**fig_kw)
    else:
        fig = ax.get_figure()
        fig.clear()

    # Create empty object array to hold all axes.  It's easiest to make it 1-d
    # so we can just append subplots upon creation, and then
    nplots = nrows * ncols
    axarr = np.empty(nplots, dtype=object)

    # Create first subplot separately, so we can share it if requested
    ax0 = fig.add_subplot(nrows, ncols, 1, **subplot_kw)
    if sharex:
        subplot_kw["sharex"] = ax0
    if sharey:
        subplot_kw["sharey"] = ax0
    axarr[0] = ax0

    # Note off-by-one counting because add_subplot uses the MATLAB 1-based
    # convention.
    for i in range(1, nplots):
        axarr[i] = fig.add_subplot(nrows, ncols, i + 1, **subplot_kw)

    if nplots > 1:
        if sharex and nrows > 1:
            for i, ax in enumerate(axarr):
                if np.ceil(float(i + 1) / ncols) < nrows:  # only last row
                    [label.set_visible(False) for label in ax.get_xticklabels()]
        if sharey and ncols > 1:
            for i, ax in enumerate(axarr):
                if (i % ncols) != 0:  # only first column
                    [label.set_visible(False) for label in ax.get_yticklabels()]

    if squeeze:
        # Reshape the array to have the final desired dimension (nrow,ncol),
        # though discarding unneeded dimensions that equal 1.  If we only have
        # one subplot, just return it instead of a 1-element array.
        if nplots == 1:
            axes = axarr[0]
        else:
            axes = axarr.reshape(nrows, ncols).squeeze()
    else:
        # returned axis array will be always 2-d, even if nrows=ncols=1
        axes = axarr.reshape(nrows, ncols)

    return fig, axes