update primefac func name

single_process_vs_multi_process/main.py

@@ -3,22 +3,23 @@ from random import randint
 from time import time
 
 # This example compare the speed of CPU-bound operations
-# when using a sequential method and also when using a 
+# when using a sequential method and also when using a
 # multi-process method with the 'multiprocessing' Python
 # package
 
 
 def calculatePrimeFactors(n):
-    primfac = []
+    prime_factors = []
     d = 2
     while d*d <= n:
         while (n % d) == 0:
-            primfac.append(d)  # supposing you want multiple factors repeated
+            # supposing you want multiple factors repeated
+            prime_factors.append(d)
             n //= d
         d += 1
         if n > 1:
-            primfac.append(n)
-    return primfac
+            prime_factors.append(n)
+    return prime_factors
 
 
 # Sequential Example
@@ -34,14 +35,13 @@ def seq_crunch():
     return f'{t1-t0:.2f}'
 
 
-
 # Multi-processing Example
 
 
 def executeProc():
- for i in range(1000):
-    rand = randint(20000, 100000000)
-    calculatePrimeFactors(rand)
+    for i in range(1000):
+        rand = randint(20000, 100000000)
+        calculatePrimeFactors(rand)
 
 
 def multi_proc_crunch():
@@ -60,11 +60,11 @@ def multi_proc_crunch():
     t1 = time()
     return f'{t1-t0:.2f}'
 
+
 if __name__ == '__main__':
     seq_time = seq_crunch()
     multi_proc_time = multi_proc_crunch()
     print('\n==================================')
-    print(f'Time to crunch prime factors sequentially: {seq_time}s')
-    print(f'Time to crunch prime factors using multi-processing: {multi_proc_time}s')
+    print(f'Crunch prime factors sequentially: {seq_time}s')
+    print(f'Crunch prime factors using multi-processing: {multi_proc_time}s')
     print('==================================')
-