code: def create_correlation_table(A, B, names_cols_A, names_cols_B):
      """
      Compute correlation coefficients and return as a DataFrame.
  
      A and B: 2d array like.
          The columns represent the different variables and the rows are
          the samples of thos variables
      names_cols_A/B : name to be added to the final pandas table
  
      return: pandas DataFrame with the corelations.Columns and Indexes
      represent the different variables of A and B (respectvely)
      """
      # corrs = np.corrcoef(np.transpose(A), np.transpose(B)
      #                     )[len(names_cols_A):, :len(names_cols_A)]
      corrs = corr2_coeff(A.T, B.T).T
  
      df_corrs = pd.DataFrame(
          corrs, columns=names_cols_A, index=names_cols_B
      )
      return df_corrs
  
file_path: mlxtend/mlxtend/plotting/pca_correlation_graph.py
imports: import matplotlib.pyplot
  import numpy
  import pandas
  from mlxtend.externals.adjust_text import adjust_text
  from mlxtend.feature_extraction import PrincipalComponentAnalysis
label_desc: Use image node_id 2 for calling a global function with example usage: create_correlation_table(A, B, names_cols_A, names_cols_B) and returns: df_corrs