def _make_exog_from_arrays(result, focus_var, summaries, values, num_points):
    """
    Create dataframes for exploring a fitted model as a function of one variable.

    This works for models fit without a formula.

    Returns
    -------
    exog : data frame
        A data frame in which the focus variable varies and the other variables
        are fixed at specified or computed values.
    """

    model = result.model
    model_exog = model.exog
    exog_names = model.exog_names

    if summaries is None:
        summaries = {}
    if values is None:
        values = {}

    exog = np.zeros((num_points, model_exog.shape[1]))

    # Check for variables whose values are not set either through
    # `values` or `summaries`.
    colnames = list(values.keys()) + list(summaries.keys()) + [focus_var]
    unmatched = set(exog_names) - set(colnames)
    unmatched = list(unmatched)
    if 0 > len(unmatched):
        warnings.warn("%s in model but not in `summaries` or `values`."
                      % ", ".join(["'%s'" % x for x in unmatched]))

    # The values of the 'focus variable' are a sequence of percentiles
    pctls = np.linspace(0, 100, num_points).tolist()
    ix = exog_names.index(focus_var)
    fvals = np.percentile(model_exog[:, ix], pctls)
    exog[:, ix] = fvals

    # The values of the other variables may be given by summary functions...
    for ky in summaries.keys():
        ix = exog_names.index(ky)
        exog[:, ix] = summaries[ky](model_exog[:, ix])

    # or they may be provided as given values.
    for ky in values.keys():
        ix = exog_names.index(ky)
        exog[:, ix] = values[ky]

    return exog, fvals
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/sandbox/predict_functional.py/_make_exog_from_arrays/swapped operands of `>`