code: def _correlation_ratio_matrix(num_df, cat_df):
      """Computes correlation ratio for all numeric-categoric pairs of columns.
  
      Args:
          num_df (DataFrame): A dataframe containing only numeric features
          cat_df (DataFrame): A dataframe containing only categorical features
  
      Returns:
          A pandas data frame
      """
      num_index = num_df.columns.values
      cat_index = cat_df.columns.values
      corr_ratio_mat = pd.DataFrame(
          [
              [
                  _correlation_ratio(cat_df[x], num_df[y])
                  for x in cat_index
              ]
              for y in num_index
          ]
      )
  
      # Assign column and row labels
      corr_ratio_mat.columns = cat_index
      corr_ratio_mat.set_index(num_index, inplace=True)
      return corr_ratio_mat
  
imports: import warnings
  import pandas
  import numpy
  from scipy.cluster import hierarchy
  from sklearn.metrics import matthews_corrcoef
  from scipy.stats import chi2_contingency
  import seaborn
  import matplotlib.pyplot
  import plotly.graph_objs
  import plotly.offline
  from data_describe.config._config import get_option
  from data_describe.misc.colors import get_p_RdBl_cmap, mpl_to_plotly_cmap
  from data_describe._widget import BaseWidget
  from data_describe.compat import _is_dataframe, _in_notebook, _requires
  from data_describe.backends import _get_viz_backend, _get_compute_backend
label_desc: Use image node_id 5 for calling a global function with example usage: _correlation_ratio_matrix(num_df, cat_df) and returns: corr_ratio_mat