code: 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 len(unmatched) > 0:
          warnings.warn(
              "%s in model but not in `summaries` or `values`."
              % ", ".join(["'%s'" % x for x in unmatched]),
              ValueWarning,
          )
  
      # 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
  
file_path: statsmodels/statsmodels/sandbox/predict_functional.py
imports: import pandas
  import patsy
  import numpy
  import warnings
  from statsmodels.tools.sm_exceptions import ValueWarning
  from statsmodels.compat.pandas import Appender
label_desc: Use image node_id 2 for calling a global function with example usage: _make_exog_from_arrays(result, focus_var, summaries, values, num_points) and returns: exog,  fvals