diff --git a/tutorials/sqlite3_howto/code/print_db_info.py b/tutorials/sqlite3_howto/code/print_db_info.py
index 285a635..ae2d671 100644
--- a/tutorials/sqlite3_howto/code/print_db_info.py
+++ b/tutorials/sqlite3_howto/code/print_db_info.py
@@ -65,9 +65,7 @@ def values_in_col(cursor, table_name, print_out=True):
     """
     cursor.execute('PRAGMA TABLE_INFO({})'.format(table_name))
     info = cursor.fetchall()
-    col_dict = dict()
-    for col in info:
-        col_dict[col[1]] = 0
+    col_dict = {col[1]: 0 for col in info}
     for col in col_dict:
         c.execute('SELECT ({0}) FROM {1} '
                   'WHERE {0} IS NOT NULL'.format(col, table_name))
diff --git a/useful_scripts/find_file.py b/useful_scripts/find_file.py
index 8cbcc4d..6663d9f 100644
--- a/useful_scripts/find_file.py
+++ b/useful_scripts/find_file.py
@@ -6,11 +6,7 @@
 import os
 
 def find_files(substring, path):
-    results = []
-    for f in os.listdir(path):
-        if substring in f:
-            results.append(os.path.join(path, f))
-    return results
+    return [os.path.join(path, f) for f in os.listdir(path) if substring in f]
     
 # E.g.
 # find_files('Untitled', '/Users/sebastian/Desktop/')
diff --git a/useful_scripts/univariate_poisson_pdf.py b/useful_scripts/univariate_poisson_pdf.py
index 30d3ae4..88afd6f 100644
--- a/useful_scripts/univariate_poisson_pdf.py
+++ b/useful_scripts/univariate_poisson_pdf.py
@@ -16,11 +16,7 @@ def likelihood_poisson(x, lam):
     Poisson distribution
     
     """
-    if x // 1 != x:
-        likelihood = 0
-    else:
-        likelihood = math.e**(-lam) * lam**(x) / math.factorial(x)
-    return likelihood
+    return 0 if x // 1 != x else math.e**(-lam) * lam**x / math.factorial(x)
 
 
 if __name__ == "__main__":
diff --git a/useful_scripts/univariate_rayleigh_pdf.py b/useful_scripts/univariate_rayleigh_pdf.py
index de0ae48..68f39e9 100644
--- a/useful_scripts/univariate_rayleigh_pdf.py
+++ b/useful_scripts/univariate_rayleigh_pdf.py
@@ -6,8 +6,7 @@ def comp_theta_mle(d):
     dataset for a Rayleigh distribution.
     
     """
-    theta = len(d) / sum([x**2  for x in d])
-    return theta    
+    return len(d) / sum(x**2 for x in d)    
 
 def likelihood_ray(x, theta):
     """