9885acac4edf3034b1b6e2335c61dd80d7abe8f9,pynets/utils.py,,collect_pandas_df_make,#Any#Any#Any#Any#,174

Before Change

                    means.append(np.nanmean(valsB))
                else:
                    means.append(np.nan)
            df_concatted_weight_means = pd.DataFrame(means).transpose()
            df_concatted_weight_means.columns = [str(col) + "_mean" for col in measures]
            df_concatted_std.columns = [str(col) + "_std_dev" for col in df_concatted_std.columns]
            result = pd.concat([df_concatted, df_concatted_std, df_concatted_weight_means], axis=1)
            df_concatted_final = result.reindex(sorted(result.columns), axis=1)

After Change

            if rand_forest is True:
                df_rnd_forest = random_forest_ensemble(df_concat.loc[:, measures])
                if network:
                    net_pick_out_path = "%s%s%s%s%s%s%s%s" % (subject_path, "/", str(ID), "_", name_of_network_pickle, "_", network, "_rand_forest")
                else:
                    net_pick_out_path = "%s%s%s%s%s%s" % (subject_path, "/", str(ID), "_", name_of_network_pickle, "_rand_forest")
                df_rnd_forest.to_pickle(net_pick_out_path)
                df_rnd_forest.to_csv(net_pick_out_path + ".csv", index=False)

Instances