diff --git a/benchmarks/dataframe_performance.cc b/benchmarks/dataframe_performance.cc
index 446eca99..f617a91c 100644
--- a/benchmarks/dataframe_performance.cc
+++ b/benchmarks/dataframe_performance.cc
@@ -82,11 +82,10 @@ int main(int, char *[]) {
 
     const auto  fourth = high_resolution_clock::now();
 
-    df.sort<double, double, double>("log_normal", sort_spec::ascen,
-                                    "exponential", sort_spec::ascen);
-    // df.sort<double, double>("log_normal", sort_spec::ascen);
-    std::cout << "Number of rows after sort: "
-              << df.get_column<double>("normal").size() << std::endl;
+    // df.sort<double, double, double>("log_normal", sort_spec::ascen,
+    //                                 "exponential", sort_spec::ascen);
+    // std::cout << "1001th value in normal column: "
+    //           << df.get_column<double>("normal")[1001] << std::endl;
 
     const auto  fifth = high_resolution_clock::now();
 
@@ -96,9 +95,9 @@ int main(int, char *[]) {
               << "Selection time: "
               << double(duration_cast<microseconds>(fourth - third).count()) / 1000000.0
               << " secs\n"
-              << "Sorting time: "
-              << double(duration_cast<microseconds>(fifth - fourth).count()) / 1000000.0
-              << " secs\n"
+              // << "Sorting time: "
+              // << double(duration_cast<microseconds>(fifth - fourth).count()) / 1000000.0
+              // << " secs\n"
               << "Overall time: "
               << double(duration_cast<microseconds>(fifth - first).count()) / 1000000.0
               << " secs"
diff --git a/benchmarks/polars_performance.py b/benchmarks/polars_performance.py
index 55c6000f..f6b9463f 100644
--- a/benchmarks/polars_performance.py
+++ b/benchmarks/polars_performance.py
@@ -33,14 +33,13 @@
 print(f"Number of rows after select: {df3.select(pl.count()).item()}")
 fourth = datetime.datetime.now()
 
-df4 = df.sort(["log_normal", "exponential"]);
-# df4 = df.sort("log_normal");
-print(f"Number of rows after sort: {df4.select(pl.count()).item()}")
+# df4 = df.sort(["log_normal", "exponential"]);
+# print(f"1001th value in normal column: {df4['normal'][1001]}")
 fifth = datetime.datetime.now()
 
 print(f"Calculation time: {(third - second).seconds}.{(third - second).microseconds} secs")
 print(f"Selection time: {(fourth - third).seconds}.{(fourth - third).microseconds} secs")
-print(f"Sorting time: {(fifth - fourth).seconds}.{(fifth - fourth).microseconds} secs")
+# print(f"Sorting time: {(fifth - fourth).seconds}.{(fifth - fourth).microseconds} secs")
 print(f"Overall time: {(fifth - first).seconds}.{(fifth - first).microseconds} secs")
 
 # ------------------------------------------------------------------------------