diff --git a/load_dataset.py b/load_dataset.py
index 576d039..8cfc184 100644
--- a/load_dataset.py
+++ b/load_dataset.py
@@ -49,9 +49,9 @@ def analisis_univariado(dfi, target=None, continuas=[], discretas=[]):
             t_stat, t_pval = ttest_ind(group1_values, group2_values, equal_var=False)
             results.append([
                 var, "Continua", f"Normal  (p={p:.7f})",
-                f"Media: {mean1:.2f} ({glabel[group1]}), {mean2:.2f} ({glabel[group2]})",
-                f"Desviación Est.: {std1:.2f} ({glabel[group1]}), {std2:.2f} ({glabel[group2]})",
-                f"Test t: p={t_pval:.3f}"
+                f"mean: {mean1:.2f}, stdev: {std1:.2f}",
+                f"mean: {mean2:.2f}, stdev: {std2:.2f}",
+                f"t Student: p={t_pval:.3f}"
             ])
         else:
             # Distribución no normal: mediana, rango intercuartil, y test Mann-Whitney
@@ -60,8 +60,8 @@ def analisis_univariado(dfi, target=None, continuas=[], discretas=[]):
             mw_stat, mw_pval = mannwhitneyu(group1_values, group2_values)
             results.append([
                 var, "Continua", f"No Normal (p={p:.7f})",
-                f"Mediana: {median1:.2f} ({glabel[group1]}), {median2:.2f} ({glabel[group2]})",
-                f"RIC: {iqr1:.2f} ({glabel[group1]}), {iqr2:.2f} ({glabel[group2]})",
+                f"Mediana: {median1:.2f}, RIC: {iqr1:.2f}",
+                f"Mediana: {median2:.2f}, RIC: {iqr2:.2f}",
                 f"Mann-Whitney: p={mw_pval:.3f}"
             ])
 
@@ -83,13 +83,13 @@ def analisis_univariado(dfi, target=None, continuas=[], discretas=[]):
         results.append([
             var, "Discreta", "N/A",
             f"Frecuencias: {freq_table.values}",
-            f"Porcentajes: {percentages.values}",
+            f"Porcentajes: {percentages.values:.1f}%",
             test_result
         ])
 
     # Crear DataFrame con los resultados
     results_df = pd.DataFrame(results, columns=[
-        "Variable", "Tipo", "Shapiro-Wilk", "Medidas descriptivas", "Estadísticas", "Resultados Prueba"
+        "Variable", "Tipo", "Shapiro-Wilk", glabel[group1], glabel[group2], "Comparación"
     ])
     return results_df