# Buggy code
def lowess(
    endog,
    exog,
    frac=2.0 / 3.0,
    it=3,
    delta=0.0,
    is_sorted=False,
    missing="drop",
    return_sorted=True,
):
    """LOWESS (Locally Weighted Scatterplot Smoothing)

    A lowess function that outs smoothed estimates of endog
    at the given exog values from points (exog, endog)

    Parameters
    ----------
    endog: 1-D numpy array
        The y-values of the observed points
    exog: 1-D numpy array
        The x-values of the observed points
    frac: float
        Between 0 and 1. The fraction of the data used
        when estimating each y-value.
    it: int
        The number of residual-based reweightings
        to perform.
    delta: float
        Distance within which to use linear-interpolation
        instead of weighted regression.
    is_sorted : bool
        If False (default), then the data will be sorted by exog before
        calculating lowess. If True, then it is assumed that the data is
        already sorted by exog.
    missing : str
        Available options are 'none', 'drop', and 'raise'. If 'none', no nan
        checking is done. If 'drop', any observations with nans are dropped.
        If 'raise', an error is raised. Default is 'drop'.
    return_sorted : bool
        If True (default), then the returned array is sorted by exog and has
        missing (nan or infinite) observations removed.
        If False, then the returned array is in the same length and the same
        sequence of observations as the input array.

    Returns
    -------
    out: ndarray, float
        The returned array is two-dimensional if return_sorted is True, and
        one dimensional if return_sorted is False.
        If return_sorted is True, then a numpy array with two columns. The
        first column contains the sorted x (exog) values and the second column
        the associated estimated y (endog) values.
        If return_sorted is False, then only the fitted values are returned,
        and the observations will be in the same order as the input arrays.

    Notes
    -----
    This lowess function implements the algorithm given in the
    reference below using local linear estimates.

    Suppose the input data has N points. The algorithm works by
    estimating the `smooth` y_i by taking the frac*N closest points
    to (x_i,y_i) based on their x values and estimating y_i
    using a weighted linear regression. The weight for (x_j,y_j)
    is tricube function applied to |x_i-x_j|.

    If it > 1, then further weighted local linear regressions
    are performed, where the weights are the same as above
    times the _lowess_bisquare function of the residuals. Each iteration
    takes approximately the same amount of time as the original fit,
    so these iterations are expensive. They are most useful when
    the noise has extremely heavy tails, such as Cauchy noise.
    Noise with less heavy-tails, such as t-distributions with df>2,
    are less problematic. The weights downgrade the influence of
    points with large residuals. In the extreme case, points whose
    residuals are larger than 6 times the median absolute residual
    are given weight 0.

    `delta` can be used to save computations. For each `x_i`, regressions
    are skipped for points closer than `delta`. The next regression is
    fit for the farthest point within delta of `x_i` and all points in
    between are estimated by linearly interpolating between the two
    regression fits.

    Judicious choice of delta can cut computation time considerably
    for large data (N > 5000). A good choice is ``delta = 0.01 * range(exog)``.

    Some experimentation is likely required to find a good
    choice of `frac` and `iter` for a particular dataset.

    References
    ----------
    Cleveland, W.S. (1979) "Robust Locally Weighted Regression
    and Smoothing Scatterplots". Journal of the American Statistical
    Association 74 (368): 829-836.

    Examples
    --------
    The below allows a comparison between how different the fits from
    lowess for different values of frac can be.

    >>> import numpy as np
    >>> import statsmodels.api as sm
    >>> lowess = sm.nonparametric.lowess
    >>> x = np.random.uniform(low = -2*np.pi, high = 2*np.pi, size=500)
    >>> y = np.sin(x) + np.random.normal(size=len(x))
    >>> z = lowess(y, x)
    >>> w = lowess(y, x, frac=1./3)

    This gives a similar comparison for when it is 0 vs not.

    >>> import numpy as np
    >>> import scipy.stats as stats
    >>> import statsmodels.api as sm
    >>> lowess = sm.nonparametric.lowess
    >>> x = np.random.uniform(low = -2*np.pi, high = 2*np.pi, size=500)
    >>> y = np.sin(x) + stats.cauchy.rvs(size=len(x))
    >>> z = lowess(y, x, frac= 1./3, it=0)
    >>> w = lowess(y, x, frac=1./3)

    """

    endog = np.asarray(endog, float)
    exog = np.asarray(exog, float)

    # Inputs should be vectors (1-D arrays) of the
    # same length.
    if exog.ndim != 1:
        raise ValueError("exog must be a vector")
    if endog.ndim != 1:
        raise ValueError("endog must be a vector")
    if endog.shape[0] != exog.shape[0]:
        raise ValueError("exog and endog must have same length")

    if missing in ["drop", "raise"]:
        # Cut out missing values
        mask_valid = np.isfinite(exog) & np.isfinite(endog)
        all_valid = np.all(mask_valid)
        if all_valid:
            y = endog
            x = exog
        else:
            if missing == "drop":
                x = exog[mask_valid]
                y = endog[mask_valid]
            else:
                raise ValueError("nan or inf found in data")
    elif missing == "none":
        y = endog
        x = exog
        all_valid = True  # we assume it's true if missing='none'
    else:
        raise ValueError("missing can only be 'none', 'drop' or 'raise'")

    if not is_sorted:
        # Sort both inputs according to the ascending order of x values
        sort_index = np.argsort(x)
        x = np.array(x[sort_index])
        y = np.array(y[sort_index])

    res = _lowess(y, x, frac=frac, it=it, delta=delta)
    _, yfitted = res.T

    if return_sorted or (all_valid and is_sorted):
        return res
    else:
        # rebuild yfitted with original indices
        # a bit messy: y might have been selected twice
        if not is_sorted:
            yfitted_ = np.empty_like(endog)
            yfitted_.fill(np.nan)
            yfitted_[sort_index] = yfitted
            yfitted = yfitted_

        if not all_valid:
            yfitted_ = np.empty_like(endog)
            yfitted_.fill(np.nan)
            yfitted_[mask_valid] = yfitted
            yfitted = yfitted_

        # we don't need to return exog anymore
        return yfitted


# Traceback (ValueError)
======================================================================
ERROR: statsmodels.nonparametric.tests.test_lowess.TestLowess.test_options
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest
self.test(*self.arg)
File "/build/buildd/statsmodels-0.5.0~ppa17~revno/debian/python-statsmodels/usr/lib/python2.7/dist-packages/statsmodels/nonparametric/tests/test_lowess.py", line 144, in test_options
return_sorted=False)
File "/build/buildd/statsmodels-0.5.0~ppa17~revno/debian/python-statsmodels/usr/lib/python2.7/dist-packages/statsmodels/nonparametric/smoothers_lowess.py", line 182, in lowess
yfitted_[mask_valid] = yfitted
ValueError: NumPy boolean array indexing assignment cannot assign 20 input values to the 17 output values where the mask is true

# Fixed code
def lowess(
    endog,
    exog,
    frac=2.0 / 3.0,
    it=3,
    delta=0.0,
    is_sorted=False,
    missing="drop",
    return_sorted=True,
):
    """LOWESS (Locally Weighted Scatterplot Smoothing)

    A lowess function that outs smoothed estimates of endog
    at the given exog values from points (exog, endog)

    Parameters
    ----------
    endog: 1-D numpy array
        The y-values of the observed points
    exog: 1-D numpy array
        The x-values of the observed points
    frac: float
        Between 0 and 1. The fraction of the data used
        when estimating each y-value.
    it: int
        The number of residual-based reweightings
        to perform.
    delta: float
        Distance within which to use linear-interpolation
        instead of weighted regression.
    is_sorted : bool
        If False (default), then the data will be sorted by exog before
        calculating lowess. If True, then it is assumed that the data is
        already sorted by exog.
    missing : str
        Available options are 'none', 'drop', and 'raise'. If 'none', no nan
        checking is done. If 'drop', any observations with nans are dropped.
        If 'raise', an error is raised. Default is 'drop'.
    return_sorted : bool
        If True (default), then the returned array is sorted by exog and has
        missing (nan or infinite) observations removed.
        If False, then the returned array is in the same length and the same
        sequence of observations as the input array.

    Returns
    -------
    out: ndarray, float
        The returned array is two-dimensional if return_sorted is True, and
        one dimensional if return_sorted is False.
        If return_sorted is True, then a numpy array with two columns. The
        first column contains the sorted x (exog) values and the second column
        the associated estimated y (endog) values.
        If return_sorted is False, then only the fitted values are returned,
        and the observations will be in the same order as the input arrays.

    Notes
    -----
    This lowess function implements the algorithm given in the
    reference below using local linear estimates.

    Suppose the input data has N points. The algorithm works by
    estimating the `smooth` y_i by taking the frac*N closest points
    to (x_i,y_i) based on their x values and estimating y_i
    using a weighted linear regression. The weight for (x_j,y_j)
    is tricube function applied to |x_i-x_j|.

    If it > 1, then further weighted local linear regressions
    are performed, where the weights are the same as above
    times the _lowess_bisquare function of the residuals. Each iteration
    takes approximately the same amount of time as the original fit,
    so these iterations are expensive. They are most useful when
    the noise has extremely heavy tails, such as Cauchy noise.
    Noise with less heavy-tails, such as t-distributions with df>2,
    are less problematic. The weights downgrade the influence of
    points with large residuals. In the extreme case, points whose
    residuals are larger than 6 times the median absolute residual
    are given weight 0.

    `delta` can be used to save computations. For each `x_i`, regressions
    are skipped for points closer than `delta`. The next regression is
    fit for the farthest point within delta of `x_i` and all points in
    between are estimated by linearly interpolating between the two
    regression fits.

    Judicious choice of delta can cut computation time considerably
    for large data (N > 5000). A good choice is ``delta = 0.01 * range(exog)``.

    Some experimentation is likely required to find a good
    choice of `frac` and `iter` for a particular dataset.

    References
    ----------
    Cleveland, W.S. (1979) "Robust Locally Weighted Regression
    and Smoothing Scatterplots". Journal of the American Statistical
    Association 74 (368): 829-836.

    Examples
    --------
    The below allows a comparison between how different the fits from
    lowess for different values of frac can be.

    >>> import numpy as np
    >>> import statsmodels.api as sm
    >>> lowess = sm.nonparametric.lowess
    >>> x = np.random.uniform(low = -2*np.pi, high = 2*np.pi, size=500)
    >>> y = np.sin(x) + np.random.normal(size=len(x))
    >>> z = lowess(y, x)
    >>> w = lowess(y, x, frac=1./3)

    This gives a similar comparison for when it is 0 vs not.

    >>> import numpy as np
    >>> import scipy.stats as stats
    >>> import statsmodels.api as sm
    >>> lowess = sm.nonparametric.lowess
    >>> x = np.random.uniform(low = -2*np.pi, high = 2*np.pi, size=500)
    >>> y = np.sin(x) + stats.cauchy.rvs(size=len(x))
    >>> z = lowess(y, x, frac= 1./3, it=0)
    >>> w = lowess(y, x, frac=1./3)

    """

    endog = np.asarray(endog, float)
    exog = np.asarray(exog, float)

    # Inputs should be vectors (1-D arrays) of the
    # same length.
    if exog.ndim != 1:
        raise ValueError("exog must be a vector")
    if endog.ndim != 1:
        raise ValueError("endog must be a vector")
    if endog.shape[0] != exog.shape[0]:
        raise ValueError("exog and endog must have same length")

    if missing in ["drop", "raise"]:
        # Cut out missing values
        mask_valid = np.isfinite(exog) & np.isfinite(endog)
        all_valid = np.all(mask_valid)
        if all_valid:
            y = endog
            x = exog
        else:
            if missing == "drop":
                x = exog[mask_valid]
                y = endog[mask_valid]
            else:
                raise ValueError("nan or inf found in data")
    elif missing == "none":
        y = endog
        x = exog
        all_valid = True  # we assume it's true if missing='none'
    else:
        raise ValueError("missing can only be 'none', 'drop' or 'raise'")

    if not is_sorted:
        # Sort both inputs according to the ascending order of x values
        sort_index = np.argsort(x)
        x = np.array(x[sort_index])
        y = np.array(y[sort_index])

    res = _lowess(y, x, frac=frac, it=it, delta=delta)
    _, yfitted = res.T

    if return_sorted or (all_valid and is_sorted):
        return res
    else:
        # rebuild yfitted with original indices
        # a bit messy: y might have been selected twice
        if not is_sorted:
            yfitted_ = np.empty_like(y)
            yfitted_.fill(np.nan)
            yfitted_[sort_index] = yfitted
            yfitted = yfitted_

        if not all_valid:
            yfitted_ = np.empty_like(endog)
            yfitted_.fill(np.nan)
            yfitted_[mask_valid] = yfitted
            yfitted = yfitted_

        # we don't need to return exog anymore
        return yfitted