realpython · ptmcg · Apr 4, 2019
diff --git a/docs/scenarios/admin.rst b/docs/scenarios/admin.rst
@@ -183,7 +183,7 @@ tests (net, CPU) fail, it will send an email.
     FROM = "webmaster@your_domain.com"
     SUBJECT = "Your domain is out of system resources!"
     text = "Go and fix your server!"
-    BODY = string.join(("From: %s" %FROM,"To: %s" %TO,"Subject: %s" %SUBJECT, "",text), "\r\n")
+    BODY = "\r\n".join(("From: %s" %FROM,"To: %s" %TO,"Subject: %s" %SUBJECT, "",text))
     server = smtplib.SMTP('127.0.0.1')
     server.sendmail(FROM, [TO], BODY)
     server.quit()

diff --git a/docs/scenarios/ml.rst b/docs/scenarios/ml.rst
@@ -108,9 +108,9 @@ We shuffle the Iris dataset and divide it into separate training and testing set
 	#predictions on the test dataset
 	pred = clf.predict(x_test)
 
-	print pred #predicted labels i.e flower species
-	print y_test #actual labels
-	print (accuracy_score(pred, y_test))*100 #prediction accuracy
+	print(pred) #predicted labels i.e flower species
+	print(y_test)#actual labels
+	print(accuracy_score(pred, y_test))*100 #prediction accuracy
 
 Since we're splitting randomly and the classifier trains on every iteration, the accuracy may vary. Running the above code gives:
 

diff --git a/docs/scenarios/scrape.rst b/docs/scenarios/scrape.rst
@@ -87,8 +87,8 @@ Let's see what we got exactly:
 
 .. code-block:: python
 
-    print 'Buyers: ', buyers
-    print 'Prices: ', prices
+    print('Buyers: ', buyers)
+    print('Prices: ', prices)
 
 ::
 

diff --git a/docs/scenarios/serialization.rst b/docs/scenarios/serialization.rst
@@ -72,7 +72,8 @@ The repr method in Python takes a single object parameter and returns a printabl
         print(repr(a))
 
         # write content to files using repr
-        with open('/tmp/file.py') as f:f.write(repr(a))
+        with open('/tmp/file.py') as f:
+            f.write(repr(a))
 
 
 ast.literal_eval
@@ -83,7 +84,8 @@ Supported data types are: strings, numbers, tuples, lists, dicts, booleans, and
 
 .. code-block:: python
 
-        with open('/tmp/file.py', 'r') as f: inp = ast.literal_eval(f.read())
+        with open('/tmp/file.py', 'r') as f:
+            inp = ast.literal_eval(f.read())
 
 ====================
 CSV file (flat data)

diff --git a/docs/scenarios/speed.rst b/docs/scenarios/speed.rst
@@ -176,17 +176,17 @@ What's the difference in speed? Let's try it!
 	#primes implemented with Python
 	import primes
 
-	print "Cython:"
+	print("Cython:")
 	t1= time.time()
-	print primesCy.primes(500)
+	print(primesCy.primes(500))
 	t2= time.time()
-	print "Cython time: %s" %(t2-t1)
-	print ""
-	print "Python"
+	print("Cython time: %s" %(t2-t1))
+	print("")
+	print("Python")
 	t1= time.time()
-	print primes.primes(500)
+	print(primes.primes(500))
 	t2= time.time()
-	print "Python time: %s" %(t2-t1)
+	print("Python time: %s" %(t2-t1))
 
 
 These lines both need a remark:

diff --git a/docs/writing/structure.rst b/docs/writing/structure.rst
@@ -790,7 +790,7 @@ compute x + 1, you have to create another integer and give it a name.
 
     my_list = [1, 2, 3]
     my_list[0] = 4
-    print my_list  # [4, 2, 3] <- The same list has changed
+    print(my_list)  # [4, 2, 3] <- The same list has changed
 
     x = 6
     x = x + 1  # The new x is another object
@@ -824,7 +824,7 @@ most idiomatic way to do this.
     nums = ""
     for n in range(20):
         nums += str(n)   # slow and inefficient
-    print nums
+    print(nums)
 
 **Better**
 
@@ -834,15 +834,15 @@ most idiomatic way to do this.
     nums = []
     for n in range(20):
         nums.append(str(n))
-    print "".join(nums)  # much more efficient
+    print("".join(nums))  # much more efficient
 
 **Best**
 
 .. code-block:: python
 
     # create a concatenated string from 0 to 19 (e.g. "012..1819")
     nums = [str(n) for n in range(20)]
-    print "".join(nums)
+    print("".join(nums))
 
 One final thing to mention about strings is that using ``join()`` is not always
 best. In the instances where you are creating a new string from a pre-determined