diff --git a/cami_opal/evaluate.py b/cami_opal/evaluate.py
index bbcda1f..123dc2d 100644
--- a/cami_opal/evaluate.py
+++ b/cami_opal/evaluate.py
@@ -91,7 +91,7 @@ def reformat_pandas(sample_id, label, braycurtis, shannon, binary_metrics, l1nor
     # convert Shannon
     pd_shannon = pd.DataFrame([shannon[rank].get_pretty_dict() for rank in shannon.keys()]).set_index(
         'rank').stack().reset_index().rename(columns={'level_1': 'metric', 0: 'value'})
-    pd_shannon['metric'].replace(['diversity', 'equitability'], [c.SHANNON_DIVERSITY, c.SHANNON_EQUIT], inplace=True)
+    pd_shannon['metric'] = pd_shannon['metric'].replace(['diversity', 'equitability'], [c.SHANNON_DIVERSITY, c.SHANNON_EQUIT])
     pd_shannon['sample'] = sample_id
     pd_shannon['tool'] = label
 
@@ -105,9 +105,9 @@ def reformat_pandas(sample_id, label, braycurtis, shannon, binary_metrics, l1nor
     pd_binary_metrics = pd.DataFrame(
         [binary_metrics[rank].get_pretty_dict() for rank in binary_metrics.keys()]).set_index(
         'rank').stack().reset_index().rename(columns={'level_1': 'metric', 0: 'value'})
-    pd_binary_metrics['metric'].replace(['fp', 'tp', 'fn', 'jaccard', 'precision', 'recall', 'f1'],
-                                        [c.FP, c.TP, c.FN, c.JACCARD, c.PRECISION, c.RECALL, c.F1_SCORE],
-                                        inplace=True)
+    pd_binary_metrics['metric'] = pd_binary_metrics['metric'].replace(
+        ['fp', 'tp', 'fn', 'jaccard', 'precision', 'recall', 'f1'],
+        [c.FP, c.TP, c.FN, c.JACCARD, c.PRECISION, c.RECALL, c.F1_SCORE])
     pd_binary_metrics['sample'] = sample_id
     pd_binary_metrics['tool'] = label
 
@@ -139,8 +139,7 @@ def reformat_pandas(sample_id, label, braycurtis, shannon, binary_metrics, l1nor
 
     if rename_as_unfiltered:
         metrics_list = pd_formatted['metric'].unique().tolist()
-        pd_formatted['metric'].replace(metrics_list, [metric + c.UNFILTERED_SUF for metric in metrics_list],
-                                       inplace=True)
+        pd_formatted['metric'] = pd_formatted['metric'].replace(metrics_list, [metric + c.UNFILTERED_SUF for metric in metrics_list])
 
     return pd_formatted
 
diff --git a/cami_opal/html_opal.py b/cami_opal/html_opal.py
index c7bacde..f8253c6 100644
--- a/cami_opal/html_opal.py
+++ b/cami_opal/html_opal.py
@@ -203,21 +203,20 @@ def get_rank_to_sample_pd(pd_metrics):
     pd_mean_over_samples_str['sample'] = '(average over samples)'
     pd_mean_over_samples_str = pd_mean_over_samples_str.reset_index().set_index(['rank', 'tool', 'sample'])
 
-    pd_grouped = pd.concat([pd_grouped, pd_mean_over_samples_str])
+    pd_grouped = pd.concat([pd_grouped, pd_mean_over_samples_str]).sort_index()
 
     # copy unifrac values to every taxonomic rank
-    pd_grouped_copy = pd_grouped.copy()
-    for index, row in pd_grouped_copy.iterrows():
-        pd_grouped.loc[index][c.UNIFRAC] = pd_grouped.loc[('rank independent', index[1], index[2])][c.UNIFRAC]
-        pd_grouped.loc[index][c.UNW_UNIFRAC] = pd_grouped.loc[('rank independent', index[1], index[2])][c.UNW_UNIFRAC]
-        pd_grouped.loc[index][c.UNIFRAC_CAMI] = pd_grouped.loc[('rank independent', index[1], index[2])][c.UNIFRAC_CAMI]
-        pd_grouped.loc[index][c.UNW_UNIFRAC_CAMI] = pd_grouped.loc[('rank independent', index[1], index[2])][c.UNW_UNIFRAC_CAMI]
+    for index, row in pd_grouped.iterrows():
+        pd_grouped.loc[index, c.UNIFRAC] = pd_grouped.loc[('rank independent', index[1], index[2])][c.UNIFRAC]
+        pd_grouped.loc[index, c.UNW_UNIFRAC] = pd_grouped.loc[('rank independent', index[1], index[2])][c.UNW_UNIFRAC]
+        pd_grouped.loc[index, c.UNIFRAC_CAMI] = pd_grouped.loc[('rank independent', index[1], index[2])][c.UNIFRAC_CAMI]
+        pd_grouped.loc[index, c.UNW_UNIFRAC_CAMI] = pd_grouped.loc[('rank independent', index[1], index[2])][c.UNW_UNIFRAC_CAMI]
     if c.UNIFRAC + c.UNFILTERED_SUF in pd_grouped.columns:
-        for index, row in pd_grouped_copy.iterrows():
-            pd_grouped.loc[index][c.UNIFRAC + c.UNFILTERED_SUF] = pd_grouped.loc[('rank independent', index[1], index[2])][c.UNIFRAC + c.UNFILTERED_SUF]
-            pd_grouped.loc[index][c.UNW_UNIFRAC + c.UNFILTERED_SUF] = pd_grouped.loc[('rank independent', index[1], index[2])][c.UNW_UNIFRAC + c.UNFILTERED_SUF]
-            pd_grouped.loc[index][c.UNIFRAC_CAMI + c.UNFILTERED_SUF] = pd_grouped.loc[('rank independent', index[1], index[2])][c.UNIFRAC_CAMI + c.UNFILTERED_SUF]
-            pd_grouped.loc[index][c.UNW_UNIFRAC_CAMI + c.UNFILTERED_SUF] = pd_grouped.loc[('rank independent', index[1], index[2])][c.UNW_UNIFRAC_CAMI + c.UNFILTERED_SUF]
+        for index, row in pd_grouped.iterrows():
+            pd_grouped.loc[index, c.UNIFRAC + c.UNFILTERED_SUF] = pd_grouped.loc[('rank independent', index[1], index[2])][c.UNIFRAC + c.UNFILTERED_SUF]
+            pd_grouped.loc[index, c.UNW_UNIFRAC + c.UNFILTERED_SUF] = pd_grouped.loc[('rank independent', index[1], index[2])][c.UNW_UNIFRAC + c.UNFILTERED_SUF]
+            pd_grouped.loc[index, c.UNIFRAC_CAMI + c.UNFILTERED_SUF] = pd_grouped.loc[('rank independent', index[1], index[2])][c.UNIFRAC_CAMI + c.UNFILTERED_SUF]
+            pd_grouped.loc[index, c.UNW_UNIFRAC_CAMI + c.UNFILTERED_SUF] = pd_grouped.loc[('rank independent', index[1], index[2])][c.UNW_UNIFRAC_CAMI + c.UNFILTERED_SUF]
 
     for (rank, sample), g in pd_grouped.groupby(['rank', 'sample']):
         rank_to_sample_pd[rank][sample] = g.reset_index().rename(columns={'tool': 'Tool'}).drop(['rank', 'sample'], axis=1).set_index('Tool').T
diff --git a/requirements.txt b/requirements.txt
index 7872c8c..8552963 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,8 +1,8 @@
-numpy==1.24.2
-matplotlib==3.7.1
-pandas==1.5.3
-jinja2==3.1.2
-bokeh==3.1.0
-seaborn==0.12.2
+numpy==2.0.1
+matplotlib==3.8.4
+pandas==2.2.2
+jinja2==3.1.4
+bokeh==3.5.1
+seaborn==0.13.2
 docker==6.1.2
-scipy==1.10.1
+scipy==1.14.0
diff --git a/version.py b/version.py
index 0fb5dff..bf66ffc 100644
--- a/version.py
+++ b/version.py
@@ -1 +1 @@
-__version__ = '1.0.12'
+__version__ = '1.0.13'