diff --git a/assets/rules_prevalence.pickle b/assets/rules_prevalence.pickle
deleted file mode 100644
index 7a29e925d..000000000
Binary files a/assets/rules_prevalence.pickle and /dev/null differ
diff --git a/capa/render/default.py b/capa/render/default.py
index 259b7ff7a..ac2c2eef5 100644
--- a/capa/render/default.py
+++ b/capa/render/default.py
@@ -6,8 +6,9 @@
 #  is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and limitations under the License.
 
-import pickle
+import json
 import collections
+from pathlib import Path
 
 import tabulate
 
@@ -84,12 +85,16 @@ def render_capabilities(doc: rd.ResultDocument, ostream: StringIO):
         +-------------------------------------------------------+-------------------------------------------------+
     """
 
-    def load_rules_prevalence(filename: str) -> dict:
-        with open(filename, "rb") as pickle_file:
-            return pickle.load(pickle_file)
+    def load_rules_prevalence(file: Path) -> dict:
+        try:
+            return json.load(file.open("r"))
+        except FileNotFoundError:
+            raise FileNotFoundError(f"File '{file}' not found.")
+        except Exception as e:
+            raise RuntimeError(f"An error occurred while loading '{file}': {e}")
 
     subrule_matches = find_subrule_matches(doc)
-    rules_prevalence = load_rules_prevalence("./assets/rules_prevalence.pickle")
+    rules_prevalence = load_rules_prevalence(Path("./assets/rules_prevalence.json"))
 
     # seperate rules based on their prevalence
     common = []
@@ -101,13 +106,13 @@ def load_rules_prevalence(filename: str) -> dict:
         count = len(rule.matches)
         matches = f"({count} matches)" if count > 1 else ""
 
-        rule_prevalence = float(rules_prevalence.get(rule.meta.name + "\n", 0))
+        rule_prevalence = float(rules_prevalence.get(rule.meta.name, 0))
         if rule_prevalence < 0:
             raise ValueError("Match probability cannot be negative")
 
         prevalences = [rutils.bold("rare"), rutils.bold("common"), "unknown"]
 
-        if rule_prevalence >= 0.05 or rule_prevalence == 0:
+        if rule_prevalence == 0 or rule_prevalence >= 0.05:
             prevalence = prevalences[2] if rule_prevalence == 0 else prevalences[1]
             common.append((rule.meta.namespace, rule.meta.name, matches, prevalence))
         else:
diff --git a/try.py b/try.py
deleted file mode 100644
index c4e6aadc3..000000000
--- a/try.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import os , subprocess, pickle, tqdm
-import pandas as pd
-
-def get_files_with_extensions(directory, extensions):
-    files = []
-    for filename in os.listdir(directory):
-        if os.path.isfile(os.path.join(directory, filename)):
-            _, ext = os.path.splitext(filename)
-            if ext.lower() in extensions:
-                files.append(os.path.join(directory, filename))
-    return files
-
-extensions = ['.exe_', '.dll_', '.sys_', '.elf_', '.raw32', '.raw64', '.cs_', '.aspx_', '.py_']
-directory = r"C:\Users\HP\Documents\GitHub\capa\tests\data"
-
-all_paths = get_files_with_extensions(directory, extensions)
-print("Total number of files to be processed ", len(all_paths))
-
-pickle_path = "./all_rules_entropy.pickle"
-write_path = "./rules_entropy.txt"
-pbar = tqdm.tqdm
-entropy = {}
-
-for file_path in pbar(all_paths):
-    cmd = ["capa", file_path]
-
-    with open(write_path, 'w') as file:
-            file.write('')
-
-    try:
-        result = subprocess.run(cmd, capture_output=True, text=True, check=True)
-    except subprocess.CalledProcessError as e:
-        print("Error running capa on " + file_path + " : " + str(e))
-
-    with open(write_path, "r") as f:
-        for line in f.readlines():
-            line.strip()
-            entropy[line] = entropy.get(line, 0) + 1
-
-    with open(pickle_path, 'wb') as pickle_file:
-        pickle.dump(entropy, pickle_file)
-
-with open(pickle_path, 'wb') as pickle_file:
-    pickle.dump(entropy, pickle_file)
-
-def save_dict_to_excel(data_dict, excel_file_path):
-    # Convert the dictionary to a pandas DataFrame
-    df = pd.DataFrame(data_dict.items())
-
-    # Save the DataFrame to an Excel file
-    df.to_excel(excel_file_path, index=False, header=["Rule", "Number of Matches"])
-
-excel_file_path = "./entropy.xlsx"
-save_dict_to_excel(entropy, excel_file_path)