Your Guide to the Python print() Function

Calling print()

The simplest example of using Python print() requires just a few keystrokes:

>>> print()

You don’t pass any arguments, but you still need to put empty parentheses at the end, which tell Python to actually execute the function rather than just refer to it by name.

This will produce an invisible newline character, which in turn will cause a blank line to appear on your screen. You can call print() multiple times like this to add vertical space. It’s just as if you were hitting Enter on your keyboard in a word processor.

>>> import os
>>> os.linesep
'\r\n'

>>> import os
>>> os.linesep
'\n'

>>> import os
>>> os.linesep
'\r'

>>> ord('r')
114
>>> ord('\r')
13

As you just saw, calling print() without arguments results in a blank line, which is a line comprised solely of the newline character. Don’t confuse this with an empty line, which doesn’t contain any characters at all, not even the newline!

You can use Python’s string literals to visualize these two:

'\n'  # Blank line
''    # Empty line

The first one is one character long, whereas the second one has no content.

>>> 'A line of text.\n'.rstrip()
'A line of text.'

In a more common scenario, you’d want to communicate some message to the end user. There are a few ways to achieve this.

First, you may pass a string literal directly to print():

>>> print('Please wait while the program is loading...')

This will print the message verbatim onto the screen.

"My favorite book is "Python Tricks""  # Wrong!

'My favorite book is "Python Tricks"'

"My favorite book is 'Python Tricks'"

"My favorite book is \"Python Tricks\""

'C:\Users\jdoe'    # Wrong!
'C:\\Users\\jdoe'

'^\\w:\\\\(?:(?:(?:[^\\\\]+)?|(?:[^\\\\]+)\\\\[^\\\\]+)*)$'

r'C:\Users\jdoe'
r'^\w:\\(?:(?:(?:[^\\]+)?|(?:[^\\]+)\\[^\\]+)*)$'

"""
This is an example
of a multi-line string
in Python.
"""

"""This is an example
of a multi-line string
in Python.
"""

"""\
This is an example
of a multi-line string
in Python.
""" 

>>> import textwrap
>>> paragraph = '''
...     This is an example
...     of a multi-line string
...     in Python.
...     '''
...
>>> print(paragraph)

    This is an example
    of a multi-line string
    in Python.

>>> print(textwrap.dedent(paragraph).strip())
This is an example
of a multi-line string
in Python.

Secondly, you could extract that message into its own variable with a meaningful name to enhance readability and promote code reuse:

>>> message = 'Please wait while the program is loading...'
>>> print(message)

Lastly, you could pass an expression, like string concatenation, to be evaluated before printing the result:

>>> import os
>>> print('Hello, ' + os.getlogin() + '! How are you?')
Hello, jdoe! How are you?

In fact, there are a dozen ways to format messages in Python. I highly encourage you to take a look at f-strings, introduced in Python 3.6, because they offer the most concise syntax of them all:

>>> import os
>>> print(f'Hello, {os.getlogin()}! How are you?')

Moreover, f-strings will prevent you from making a common mistake, which is forgetting to type cast concatenated operands. Python is a strongly typed language, which means it won’t allow you to do this:

>>> 'My age is ' + 42
Traceback (most recent call last):
  File "<input>", line 1, in <module>
    'My age is ' + 42
TypeError: can only concatenate str (not "int") to str

That’s wrong because adding numbers to strings doesn’t make sense. You need to explicitly convert the number to string first, in order to join them together:

>>> 'My age is ' + str(42)
'My age is 42'

Unless you handle such errors yourself, the Python interpreter will let you know about a problem by showing a traceback.

>>> str(3.14)
'3.14'

As with any function, it doesn’t matter whether you pass a literal, a variable, or an expression. Unlike many other functions, however, print() will accept anything regardless of its type.

So far, you only looked at the string, but how about other data types? Let’s try literals of different built-in types and see what comes out:

>>> print(42)                            # <class 'int'>
42
>>> print(3.14)                          # <class 'float'>
3.14
>>> print(1 + 2j)                        # <class 'complex'>
(1+2j)
>>> print(True)                          # <class 'bool'>
True
>>> print([1, 2, 3])                     # <class 'list'>
[1, 2, 3]
>>> print((1, 2, 3))                     # <class 'tuple'>
(1, 2, 3)
>>> print({'red', 'green', 'blue'})      # <class 'set'>
{'red', 'green', 'blue'}
>>> print({'name': 'Alice', 'age': 42})  # <class 'dict'>
{'name': 'Alice', 'age': 42}
>>> print('hello')                       # <class 'str'>
hello

Watch out for the None constant, though. Despite being used to indicate an absence of a value, it will show up as 'None' rather than an empty string:

>>> print(None)
None

How does print() know how to work with all these different types? Well, the short answer is that it doesn’t. It implicitly calls str() behind the scenes to type cast any object into a string. Afterward, it treats strings in a uniform way.

Later in this tutorial, you’ll learn how to use this mechanism for printing custom data types such as your classes.

Okay, you’re now able to call print() with a single argument or without any arguments. You know how to print fixed or formatted messages onto the screen. The next subsection will expand on message formatting a little bit.

# Python 2
print
print 'Please wait...'
print 'Hello, %s! How are you?' % os.getlogin()
print 'Hello, %s. Your age is %d.' % (name, age)

>>> # Python 2
>>> print 'Please wait...'
Please wait...
>>> print('Please wait...')
Please wait...

>>> # Python 2
>>> print 'My name is', 'John'
My name is John
>>> print('My name is', 'John')
('My name is', 'John')

>>> # Python 2
>>> print('Please wait...')
Please wait...
>>> print('Please wait...',)  # Notice the comma
('Please wait...',)

Separating Multiple Arguments

You saw print() called without any arguments to produce a blank line and then called with a single argument to display either a fixed or a formatted message.

However, it turns out that this function can accept any number of positional arguments, including zero, one, or more arguments. That’s very handy in a common case of message formatting, where you’d want to join a few elements together.

>>> def div(a, b):
...     return a / b
...
>>> div(a=3, b=4)
0.75

>>> div(b=4, a=3)
0.75

>>> div(3, 4)
0.75
>>> div(4, 3)
1.3333333333333333

Let’s have a look at this example:

>>> import os
>>> print('My name is', os.getlogin(), 'and I am', 42)
My name is jdoe and I am 42

print() concatenated all four arguments passed to it, and it inserted a single space between them so that you didn’t end up with a squashed message like 'My name isjdoeand I am42'.

Notice that it also took care of proper type casting by implicitly calling str() on each argument before joining them together. If you recall from the previous subsection, a naïve concatenation may easily result in an error due to incompatible types:

>>> print('My age is: ' + 42)
Traceback (most recent call last):
  File "<input>", line 1, in <module>
    print('My age is: ' + 42)
TypeError: can only concatenate str (not "int") to str

Apart from accepting a variable number of positional arguments, print() defines four named or keyword arguments, which are optional since they all have default values. You can view their brief documentation by calling help(print) from the interactive interpreter.

Let’s focus on sep just for now. It stands for separator and is assigned a single space (' ') by default. It determines the value to join elements with.

It has to be either a string or None, but the latter has the same effect as the default space:

>>> print('hello', 'world', sep=None)
hello world
>>> print('hello', 'world', sep=' ')
hello world
>>> print('hello', 'world')
hello world

If you wanted to suppress the separator completely, you’d have to pass an empty string ('') instead:

>>> print('hello', 'world', sep='')
helloworld

You may want print() to join its arguments as separate lines. In that case, simply pass the escaped newline character described earlier:

>>> print('hello', 'world', sep='\n')
hello
world

A more useful example of the sep parameter would be printing something like file paths:

>>> print('home', 'user', 'documents', sep='/')
home/user/documents

Remember that the separator comes between the elements, not around them, so you need to account for that in one way or another:

>>> print('/home', 'user', 'documents', sep='/')
/home/user/documents
>>> print('', 'home', 'user', 'documents', sep='/')
/home/user/documents

Specifically, you can insert a slash character (/) into the first positional argument, or use an empty string as the first argument to enforce the leading slash.

>>> print(' '.join(['jdoe is', 42, 'years old']))
Traceback (most recent call last):
  File "<input>", line 1, in <module>
    print(','.join(['jdoe is', 42, 'years old']))
TypeError: sequence item 1: expected str instance, int found

>>> print(*['jdoe is', 42, 'years old'])
jdoe is 42 years old

One more interesting example could be exporting data to a comma-separated values (CSV) format:

>>> print(1, 'Python Tricks', 'Dan Bader', sep=',')
1,Python Tricks,Dan Bader

This wouldn’t handle edge cases such as escaping commas correctly, but for simple use cases, it should do. The line above would show up in your terminal window. In order to save it to a file, you’d have to redirect the output. Later in this section, you’ll see how to use print() to write text to files straight from Python.

Finally, the sep parameter isn’t constrained to a single character only. You can join elements with strings of any length:

>>> print('node', 'child', 'child', sep=' -> ')
node -> child -> child

In the upcoming subsections, you’ll explore the remaining keyword arguments of the print() function.

>>> # Python 2
>>> import os
>>> print 'My name is', os.getlogin(), 'and I am', 42
My name is jdoe and I am 42

>>> # Python 2
>>> import os
>>> print('My name is', os.getlogin(), 'and I am', 42)
('My name is', 'jdoe', 'and I am', 42)

>>> # Python 2
>>> import os
>>> print 'My name is %s and I am %d' % (os.getlogin(), 42)
My name is jdoe and I am 42

Preventing Line Breaks

Sometimes you don’t want to end your message with a trailing newline so that subsequent calls to print() will continue on the same line. Classic examples include updating the progress of a long-running operation or prompting the user for input. In the latter case, you want the user to type in the answer on the same line:

Are you sure you want to do this? [y/n] y

Many programming languages expose functions similar to print() through their standard libraries, but they let you decide whether to add a newline or not. For example, in Java and C#, you have two distinct functions, while other languages require you to explicitly append \n at the end of a string literal.

Here are a few examples of syntax in such languages:

In contrast, Python’s print() function always adds \n without asking, because that’s what you want in most cases. To disable it, you can take advantage of yet another keyword argument, end, which dictates what to end the line with.

In terms of semantics, the end parameter is almost identical to the sep one that you saw earlier:

• It must be a string or None.
• It can be arbitrarily long.
• It has a default value of '\n'.
• If equal to None, it’ll have the same effect as the default value.
• If equal to an empty string (''), it’ll suppress the newline.

Now you understand what’s happening under the hood when you’re calling print() without arguments. Since you don’t provide any positional arguments to the function, there’s nothing to be joined, and so the default separator isn’t used at all. However, the default value of end still applies, and a blank line shows up.

>>> print('line1\nline2\nline3')
line1
line2
line3

>>> print('line1\r\nline2\r\nline3')


line3

To disable the newline, you must specify an empty string through the end keyword argument:

print('Checking file integrity...', end='')
# (...)
print('ok')

Even though these are two separate print() calls, which can execute a long time apart, you’ll eventually see only one line. First, it’ll look like this:

Checking file integrity...

However, after the second call to print(), the same line will appear on the screen as:

Checking file integrity...ok

As with sep, you can use end to join individual pieces into a big blob of text with a custom separator. Instead of joining multiple arguments, however, it’ll append text from each function call to the same line:

print('The first sentence', end='. ')
print('The second sentence', end='. ')
print('The last sentence.')

These three instructions will output a single line of text:

The first sentence. The second sentence. The last sentence.

You can mix the two keyword arguments:

print('Mercury', 'Venus', 'Earth', sep=', ', end=', ')
print('Mars', 'Jupiter', 'Saturn', sep=', ', end=', ')
print('Uranus', 'Neptune', 'Pluto', sep=', ')

Not only do you get a single line of text, but all items are separated with a comma:

Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune, Pluto

There’s nothing to stop you from using the newline character with some extra padding around it:

print('Printing in a Nutshell', end='\n * ')
print('Calling Print', end='\n * ')
print('Separating Multiple Arguments', end='\n * ')
print('Preventing Line Breaks')

It would print out the following piece of text:

Printing in a Nutshell
 * Calling Print
 * Separating Multiple Arguments
 * Preventing Line Breaks

As you can see, the end keyword argument will accept arbitrary strings.

>>> with open('file.txt') as file_object:
...     for line in file_object:
...         print(line)
...
Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod

tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam,

quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo

print(line.rstrip())

>>> with open('file.txt') as file_object:
...     for line in file_object:
...         print(line, end='')
...
Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod
tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam,
quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo

You’re getting more acquainted with printing in Python, but there’s still a lot of useful information ahead. In the upcoming subsection, you’ll learn how to intercept and redirect the print() function’s output.

print 'hello world',

print 'BEFORE'
print 'hello',
print 'AFTER'

BEFORE
hello AFTER

import sys
print 'BEFORE'
sys.stdout.write('hello')
print 'AFTER' 

BEFORE
helloAFTER

Printing to a File

Believe it or not, print() doesn’t know how to turn messages into text on your screen, and frankly it doesn’t need to. That’s a job for lower-level layers of code, which understand bytes and know how to push them around.

print() is an abstraction over these layers, providing a convenient interface that merely delegates the actual printing to a stream or file-like object. A stream can be any file on your disk, a network socket, or perhaps an in-memory buffer.

In addition to this, there are three standard streams provided by the operating system:

1. stdin: standard input
2. stdout: standard output
3. stderr: standard error

Standard StreamsShow/Hide

Standard output is what you see in the terminal when you run various command-line programs including your own Python scripts:

Shell

$ cat hello.py 
print('This will appear on stdout')
$ python hello.py
This will appear on stdout

Unless otherwise instructed, print() will default to writing to standard output. However, you can tell your operating system to temporarily swap out stdout for a file stream, so that any output ends up in that file rather than the screen:

Shell

$ python hello.py > file.txt
$ cat file.txt
This will appear on stdout

That’s called stream redirection.

The standard error is similar to stdout in that it also shows up on the screen. Nonetheless, it’s a separate stream, whose purpose is to log error messages for diagnostics. By redirecting one or both of them, you can keep things clean.

Note: To redirect stderr, you need to know about file descriptors, also known as file handles.

They’re arbitrary, albeit constant, numbers associated with standard streams. Below, you’ll find a summary of the file descriptors for a family of POSIX-compliant operating systems:

Stream	File Descriptor
stdin	0
stdout	1
stderr	2

Knowing those descriptors allows you to redirect one or more streams at a time:

Command	Description
./program > out.txt	Redirect stdout
./program 2> err.txt	Redirect stderr
./program > out.txt 2> err.txt	Redirect stdout and stderr to separate files
./program &> out_err.txt	Redirect stdout and stderr to the same file

Note that > is the same as 1>.

Some programs use different coloring to distinguish between messages printed to stdout and stderr:

While both stdout and stderr are write-only, stdin is read-only. You can think of standard input as your keyboard, but just like with the other two, you can swap out stdin for a file to read data from.

In Python, you can access all standard streams through the built-in sys module:

>>> import sys
>>> sys.stdin
<_io.TextIOWrapper name='<stdin>' mode='r' encoding='UTF-8'>
>>> sys.stdin.fileno()
0
>>> sys.stdout
<_io.TextIOWrapper name='<stdout>' mode='w' encoding='UTF-8'>
>>> sys.stdout.fileno()
1
>>> sys.stderr
<_io.TextIOWrapper name='<stderr>' mode='w' encoding='UTF-8'>
>>> sys.stderr.fileno()
2

As you can see, these predefined values resemble file-like objects with mode and encoding attributes as well as .read() and .write() methods among many others.

By default, print() is bound to sys.stdout through its file argument, but you can change that. Use that keyword argument to indicate a file that was open in write or append mode, so that messages go straight to it:

with open('file.txt', mode='w') as file_object:
    print('hello world', file=file_object)

This will make your code immune to stream redirection at the operating system level, which might or might not be desired.

For more information on working with files in Python, you can check out Reading and Writing Files in Python (Guide).

with open('file.dat', 'wb') as file_object:
    file_object.write(bytes(4))
    file_object.write(b'\xff')

>>> import sys
>>> sys.stdout.write(bytes(4))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: write() argument must be str, not bytes

>>> import sys
>>> num_bytes_written = sys.stdout.buffer.write(b'\x41\x0a')
A

Note that print() has no control over character encoding. It’s the stream’s responsibility to encode received Unicode strings into bytes correctly. In most cases, you won’t set the encoding yourself, because the default UTF-8 is what you want. If you really need to, perhaps for legacy systems, you can use the encoding argument of open():

with open('file.txt', mode='w', encoding='iso-8859-1') as file_object:
    print('über naïve café', file=file_object)

Instead of a real file existing somewhere in your file system, you can provide a fake one, which would reside in your computer’s memory. You’ll use this technique later for mocking print() in unit tests:

>>> import io
>>> fake_file = io.StringIO()
>>> print('hello world', file=fake_file)
>>> fake_file.getvalue()
'hello world\n'

If you got to this point, then you’re left with only one keyword argument in print(), which you’ll see in the next subsection. It’s probably the least used of them all. Nevertheless, there are times when it’s absolutely necessary.

with open('file.txt', mode='w') as file_object:
    print >> file_object, 'hello world'

with open('file.dat', mode='wb') as file_object:
    print >> file_object, '\x41\x0a'

>>> with open('file.txt', mode='w') as file_object:
...     unicode_text = u'\xfcber na\xefve caf\xe9'
...     print >> file_object, unicode_text
... 
Traceback (most recent call last):
  File "<stdin>", line 3, in <module>
UnicodeEncodeError: 'ascii' codec can't encode character u'\xfc'...

unicode_literal = u'\xfcber na\xefve caf\xe9'
string_literal = '\xc3\xbcber na\xc3\xafve caf\xc3\xa9'

#!/usr/bin/env python2
# -*- coding: utf-8 -*-

unescaped_unicode_literal = u'über naïve café'
unescaped_string_literal = 'über naïve café'

with open('file.txt', mode='w') as file_object:
    unicode_text = u'\xfcber na\xefve caf\xe9'
    encoded_text = unicode_text.encode('utf-8')
    print >> file_object, encoded_text

import codecs

with codecs.open('file.txt', 'w', encoding='utf-8') as file_object:
    unicode_text = u'\xfcber na\xefve caf\xe9'
    print >> file_object, unicode_text

Buffering print() Calls

In the previous subsection, you learned that print() delegates printing to a file-like object such as sys.stdout. Some streams, however, buffer certain I/O operations to enhance performance, which can get in the way. Let’s take a look at an example.

Imagine you were writing a countdown timer, which should append the remaining time to the same line every second:

3...2...1...Go!

Your first attempt may look something like this:

import time

num_seconds = 3
for countdown in reversed(range(num_seconds + 1)):
    if countdown > 0:
        print(countdown, end='...')
        time.sleep(1)
    else:
        print('Go!')

As long as the countdown variable is greater than zero, the code keeps appending text without a trailing newline and then goes to sleep for one second. Finally, when the countdown is finished, it prints Go! and terminates the line.

Unexpectedly, instead of counting down every second, the program idles wastefully for three seconds, and then suddenly prints the entire line at once:

That’s because the operating system buffers subsequent writes to the standard output in this case. You need to know that there are three kinds of streams with respect to buffering:

1. Unbuffered
2. Line-buffered
3. Block-buffered

Unbuffered is self-explanatory, that is, no buffering is taking place, and all writes have immediate effect. A line-buffered stream waits before firing any I/O calls until a line break appears somewhere in the buffer, whereas a block-buffered one simply allows the buffer to fill up to a certain size regardless of its content. Standard output is both line-buffered and block-buffered, depending on which event comes first.

Buffering helps to reduce the number of expensive I/O calls. Think about sending messages over a high-latency network, for example. When you connect to a remote server to execute commands over the SSH protocol, each of your keystrokes may actually produce an individual data packet, which is orders of magnitude bigger than its payload. What an overhead! It would make sense to wait until at least a few characters are typed and then send them together. That’s where buffering steps in.

On the other hand, buffering can sometimes have undesired effects as you just saw with the countdown example. To fix it, you can simply tell print() to forcefully flush the stream without waiting for a newline character in the buffer using its flush flag:

print(countdown, end='...', flush=True)

That’s all. Your countdown should work as expected now, but don’t take my word for it. Go ahead and test it to see the difference.

Congratulations! At this point, you’ve seen examples of calling print() that cover all of its parameters. You know their purpose and when to use them. Understanding the signature is only the beginning, however. In the upcoming sections, you’ll see why.

import time
import sys

num_seconds = 3
for countdown in reversed(range(num_seconds + 1)):
    if countdown > 0:
        sys.stdout.write('%s...' % countdown)
        sys.stdout.flush()
        time.sleep(1)
    else:
        print 'Go!'

$ python2 -u countdown.py
$ PYTHONUNBUFFERED=1 python2 countdown.py

>>> from __future__ import print_function
>>> help(print)
Help on built-in function print in module __builtin__:

print(...)
    print(value, ..., sep=' ', end='\n', file=sys.stdout)

Printing Custom Data Types

Up until now, you only dealt with built-in data types such as strings and numbers, but you’ll often want to print your own abstract data types. Let’s have a look at different ways of defining them.

For simple objects without any logic, whose purpose is to carry data, you’ll typically take advantage of namedtuple, which is available in the standard library. Named tuples have a neat textual representation out of the box:

>>> from collections import namedtuple
>>> Person = namedtuple('Person', 'name age')
>>> jdoe = Person('John Doe', 42)
>>> print(jdoe)
Person(name='John Doe', age=42)

That’s great as long as holding data is enough, but in order to add behaviors to the Person type, you’ll eventually need to define a class. Take a look at this example:

class Person:
    def __init__(self, name, age):
        self.name, self.age = name, age

If you now create an instance of the Person class and try to print it, you’ll get this bizarre output, which is quite different from the equivalent namedtuple:

>>> jdoe = Person('John Doe', 42)
>>> print(jdoe)
<__main__.Person object at 0x7fcac3fed1d0>

It’s the default representation of objects, which comprises their address in memory, the corresponding class name and a module in which they were defined. You’ll fix that in a bit, but just for the record, as a quick workaround you could combine namedtuple and a custom class through inheritance:

from collections import namedtuple

class Person(namedtuple('Person', 'name age')):
    pass

Your Person class has just become a specialized kind of namedtuple with two attributes, which you can customize.

def delta(a, b, c):
    ...

That’s better than a plain namedtuple, because not only do you get printing right for free, but you can also add custom methods and properties to the class. However, it solves one problem while introducing another. Remember that tuples, including named tuples, are immutable in Python, so they can’t change their values once created.

It’s true that designing immutable data types is desirable, but in many cases, you’ll want them to allow for change, so you’re back with regular classes again.

>>> from dataclasses import dataclass
>>> @dataclass
... class Person:
...     name: str
...     age: int
...     
...     def celebrate_birthday(self):
...         self.age += 1
... 
>>> jdoe = Person('John Doe', 42)
>>> jdoe.celebrate_birthday()
>>> print(jdoe)
Person(name='John Doe', age=43)

From earlier subsections, you already know that print() implicitly calls the built-in str() function to convert its positional arguments into strings. Indeed, calling str() manually against an instance of the regular Person class yields the same result as printing it:

>>> jdoe = Person('John Doe', 42)
>>> str(jdoe)
'<__main__.Person object at 0x7fcac3fed1d0>'

str(), in turn, looks for one of two magic methods within the class body, which you typically implement. If it doesn’t find one, then it falls back to the ugly default representation. Those magic methods are, in order of search:

1. def __str__(self)
2. def __repr__(self)

The first one is recommended to return a short, human-readable text, which includes information from the most relevant attributes. After all, you don’t want to expose sensitive data, such as user passwords, when printing objects.

However, the other one should provide complete information about an object, to allow for restoring its state from a string. Ideally, it should return valid Python code, so that you can pass it directly to eval():

>>> repr(jdoe)
"Person(name='John Doe', age=42)"
>>> type(eval(repr(jdoe)))
<class '__main__.Person'>

Notice the use of another built-in function, repr(), which always tries to call .__repr__() in an object, but falls back to the default representation if it doesn’t find that method.

class User:
    def __init__(self, login, password):
        self.login = login
        self.password = password

    def __str__(self):
        return self.login

    def __repr__(self):
        return f"User('{self.login}', '{self.password}')"

>>> user = User('jdoe', 's3cret')
>>> print(user)
jdoe

>>> print([user])
[User('jdoe', 's3cret')]

Python gives you a lot of freedom when it comes to defining your own data types if none of the built-in ones meet your needs. Some of them, such as named tuples and data classes, offer string representations that look good without requiring any work on your part. Still, for the most flexibility, you’ll have to define a class and override its magic methods described above.

class User(object):
    def __init__(self, login, password):
        self.login = login
        self.password = password

    def __unicode__(self):
        return self.login

    def __str__(self):
        return unicode(self).encode('utf-8')

    def __repr__(self):
        user = u"User('%s', '%s')" % (self.login, self.password)
        return user.encode('unicode_escape')

>>> user = User(u'\u043d\u0438\u043a\u0438\u0442\u0430', u's3cret')
>>> print user
đŻđŞđ║đŞĐéđ░

>>> user = User(u'\u043d\u0438\u043a\u0438\u0442\u0430', u's3cret')
>>> print user
никита

>>> class User(object):
...     def __init__(self, login, password):
...         self.login = login
...         self.password = password
...     
...     def __bytes__(self):  # Python 3
...         return self.login.encode('utf-8')
...
>>> user = User(u'\u043d\u0438\u043a\u0438\u0442\u0430', u's3cret')
>>> bytes(user)
b'\xd0\xbd\xd0\xb8\xd0\xba\xd0\xb8\xd1\x82\xd0\xb0'

Print Is a Function in Python 3

You’ve seen that print() is a function in Python 3. More specifically, it’s a built-in function, which means that you don’t need to import it from anywhere:

>>> print
<built-in function print>

It’s always available in the global namespace so that you can call it directly, but you can also access it through a module from the standard library:

>>> import builtins
>>> builtins.print
<built-in function print>

This way, you can avoid name collisions with custom functions. Let’s say you wanted to redefine print() so that it doesn’t append a trailing newline. At the same time, you wanted to rename the original function to something like println():

>>> import builtins
>>> println = builtins.print
>>> def print(*args, **kwargs):
...     builtins.print(*args, **kwargs, end='')
...
>>> println('hello')
hello
>>> print('hello\n')
hello

Now you have two separate printing functions just like in the Java programming language. You’ll define custom print() functions in the mocking section later as well. Also, note that you wouldn’t be able to overwrite print() in the first place if it wasn’t a function.

On the other hand, print() isn’t a function in the mathematical sense, because it doesn’t return any meaningful value other than the implicit None:

>>> value = print('hello world')
hello world
>>> print(value)
None

Such functions are, in fact, procedures or subroutines that you call to achieve some kind of side-effect, which ultimately is a change of a global state. In the case of print(), that side-effect is showing a message on the standard output or writing to a file.

Because print() is a function, it has a well-defined signature with known attributes. You can quickly find its documentation using the editor of your choice, without having to remember some weird syntax for performing a certain task.

Besides, functions are easier to extend. Adding a new feature to a function is as easy as adding another keyword argument, whereas changing the language to support that new feature is much more cumbersome. Think of stream redirection or buffer flushing, for example.

Another benefit of print() being a function is composability. Functions are so-called first-class objects or first-class citizens in Python, which is a fancy way of saying they’re values just like strings or numbers. This way, you can assign a function to a variable, pass it to another function, or even return one from another. print() isn’t different in this regard. For instance, you can take advantage of it for dependency injection:

def download(url, log=print):
    log(f'Downloading {url}')
    # ...

def custom_print(*args):
    pass  # Do not print anything

download('/js/app.js', log=custom_print)

Here, the log parameter lets you inject a callback function, which defaults to print() but can be any callable. In this example, printing is completely disabled by substituting print() with a dummy function that does nothing.

Composition allows you to combine a few functions into a new one of the same kind. Let’s see this in action by specifying a custom error() function that prints to the standard error stream and prefixes all messages with a given log level:

>>> from functools import partial
>>> import sys
>>> redirect = lambda function, stream: partial(function, file=stream)
>>> prefix = lambda function, prefix: partial(function, prefix)
>>> error = prefix(redirect(print, sys.stderr), '[ERROR]')
>>> error('Something went wrong')
[ERROR] Something went wrong

This custom function uses partial functions to achieve the desired effect. It’s an advanced concept borrowed from the functional programming paradigm, so you don’t need to go too deep into that topic for now. However, if you’re interested in this topic, I recommend taking a look at the functools module.

Unlike statements, functions are values. That means you can mix them with expressions, in particular, lambda expressions. Instead of defining a full-blown function to replace print() with, you can make an anonymous lambda expression that calls it:

>>> download('/js/app.js', lambda msg: print('[INFO]', msg))
[INFO] Downloading /js/app.js

However, because a lambda expression is defined in place, there’s no way of referring to it elsewhere in the code.

Another kind of expression is a ternary conditional expression:

>>> user = 'jdoe'
>>> print('Hi!') if user is None else print(f'Hi, {user}.')
Hi, jdoe.

Python has both conditional statements and conditional expressions. The latter is evaluated to a single value that can be assigned to a variable or passed to a function. In the example above, you’re interested in the side-effect rather than the value, which evaluates to None, so you simply ignore it.

As you can see, functions allow for an elegant and extensible solution, which is consistent with the rest of the language. In the next subsection, you’ll discover how not having print() as a function caused a lot of headaches.

print Was a Statement in Python 2

A statement is an instruction that may evoke a side-effect when executed but never evaluates to a value. In other words, you wouldn’t be able to print a statement or assign it to a variable like this:

result = print 'hello world'

That’s a syntax error in Python 2.

Here are a few more examples of statements in Python:

• assignment: =
• conditional: if
• loop: while
• assertion: assert

# Python 3.8+
values = [y := f(x), y**2, y**3]

y = f(x)
values = [y, y**2, y**3]

Statements are usually comprised of reserved keywords such as if, for, or print that have fixed meaning in the language. You can’t use them to name your variables or other symbols. That’s why redefining or mocking the print statement isn’t possible in Python 2. You’re stuck with what you get.

Furthermore, you can’t print from anonymous functions, because statements aren’t accepted in lambda expressions:

>>> lambda: print 'hello world'
  File "<stdin>", line 1
    lambda: print 'hello world'
                ^
SyntaxError: invalid syntax

The syntax of the print statement is ambiguous. Sometimes you can add parentheses around the message, and they’re completely optional:

>>> print 'Please wait...'
Please wait...
>>> print('Please wait...')
Please wait...

At other times they change how the message is printed:

>>> print 'My name is', 'John'
My name is John
>>> print('My name is', 'John')
('My name is', 'John')

String concatenation can raise a TypeError due to incompatible types, which you have to handle manually, for example:

>>> values = ['jdoe', 'is', 42, 'years old']
>>> print ' '.join(map(str, values))
jdoe is 42 years old

Compare this with similar code in Python 3, which leverages sequence unpacking:

>>> values = ['jdoe', 'is', 42, 'years old']
>>> print(*values)  # Python 3
jdoe is 42 years old

There aren’t any keyword arguments for common tasks such as flushing the buffer or stream redirection. You need to remember the quirky syntax instead. Even the built-in help() function isn’t that helpful with regards to the print statement:

>>> help(print)
  File "<stdin>", line 1
    help(print)
             ^
SyntaxError: invalid syntax

Trailing newline removal doesn’t work quite right, because it adds an unwanted space. You can’t compose multiple print statements together, and, on top of that, you have to be extra diligent about character encoding.

The list of problems goes on and on. If you’re curious, you can jump back to the previous section and look for more detailed explanations of the syntax in Python 2.

However, you can mitigate some of those problems with a much simpler approach. It turns out the print() function was backported to ease the migration to Python 3. You can import it from a special __future__ module, which exposes a selection of language features released in later Python versions.

>>> import __future__
>>> __future__.all_feature_names
['nested_scopes',
 'generators',
 'division',
 'absolute_import',
 'with_statement',
 'print_function',
 'unicode_literals']

To enable the print() function in Python 2, you need to add this import statement at the beginning of your source code:

from __future__ import print_function

From now on the print statement is no longer available, but you have the print() function at your disposal. Note that it isn’t the same function like the one in Python 3, because it’s missing the flush keyword argument, but the rest of the arguments are the same.

Other than that, it doesn’t spare you from managing character encodings properly.

Here’s an example of calling the print() function in Python 2:

>>> from __future__ import print_function
>>> import sys
>>> print('I am a function in Python', sys.version_info.major)
I am a function in Python 2

You now have an idea of how printing in Python evolved and, most importantly, understand why these backward-incompatible changes were necessary. Knowing this will surely help you become a better Python programmer.

Pretty-Printing Nested Data Structures

Computer languages allow you to represent data as well as executable code in a structured way. Unlike Python, however, most languages give you a lot of freedom in using whitespace and formatting. This can be useful, for example in compression, but it sometimes leads to less readable code.

Pretty-printing is about making a piece of data or code look more appealing to the human eye so that it can be understood more easily. This is done by indenting certain lines, inserting newlines, reordering elements, and so forth.

Python comes with the pprint module in its standard library, which will help you in pretty-printing large data structures that don’t fit on a single line. Because it prints in a more human-friendly way, many popular REPL tools, including JupyterLab and IPython, use it by default in place of the regular print() function.

In [1]: %pprint
Pretty printing has been turned OFF
In [2]: %pprint
Pretty printing has been turned ON

If you don’t care about not having access to the original print() function, then you can replace it with pprint() in your code using import renaming:

>>> from pprint import pprint as print
>>> print
<function pprint at 0x7f7a775a3510>

Personally, I like to have both functions at my fingertips, so I’d rather use something like pp as a short alias:

from pprint import pprint as pp

At first glance, there’s hardly any difference between the two functions, and in some cases there’s virtually none:

>>> print(42)
42
>>> pp(42)
42
>>> print('hello')
hello
>>> pp('hello')
'hello'  # Did you spot the difference?

That’s because pprint() calls repr() instead of the usual str() for type casting, so that you may evaluate its output as Python code if you want to. The differences become apparent as you start feeding it more complex data structures:

>>> data = {'powers': [x**10 for x in range(10)]}
>>> pp(data)
{'powers': [0,
            1,
            1024,
            59049,
            1048576,
            9765625,
            60466176,
            282475249,
            1073741824,
            3486784401]}

The function applies reasonable formatting to improve readability, but you can customize it even further with a couple of parameters. For example, you may limit a deeply nested hierarchy by showing an ellipsis below a given level:

>>> cities = {'USA': {'Texas': {'Dallas': ['Irving']}}}
>>> pp(cities, depth=3)
{'USA': {'Texas': {'Dallas': [...]}}}

The ordinary print() also uses ellipses but for displaying recursive data structures, which form a cycle, to avoid stack overflow error:

>>> items = [1, 2, 3]
>>> items.append(items)
>>> print(items)
[1, 2, 3, [...]]

However, pprint() is more explicit about it by including the unique identity of a self-referencing object:

>>> pp(items)
[1, 2, 3, <Recursion on list with id=140635757287688>]
>>> id(items)
140635757287688

The last element in the list is the same object as the entire list.

>>> import reprlib
>>> reprlib.repr([x**10 for x in range(10)])
'[0, 1, 1024, 59049, 1048576, 9765625, ...]'

pprint() automatically sorts dictionary keys for you before printing, which allows for consistent comparison. When you’re comparing strings, you often don’t care about a particular order of serialized attributes. Anyways, it’s always best to compare actual dictionaries before serialization.

Dictionaries often represent JSON data, which is widely used on the Internet. To correctly serialize a dictionary into a valid JSON-formatted string, you can take advantage of the json module. It too has pretty-printing capabilities:

>>> import json
>>> data = {'username': 'jdoe', 'password': 's3cret'}
>>> ugly = json.dumps(data)
>>> pretty = json.dumps(data, indent=4, sort_keys=True)
>>> print(ugly)
{"username": "jdoe", "password": "s3cret"}
>>> print(pretty)
{
    "password": "s3cret",
    "username": "jdoe"
}

Notice, however, that you need to handle printing yourself, because it’s not something you’d typically want to do. Similarly, the pprint module has an additional pformat() function that returns a string, in case you had to do something other than printing it.

Surprisingly, the signature of pprint() is nothing like the print() function’s one. You can’t even pass more than one positional argument, which shows how much it focuses on printing data structures.

Adding Colors With ANSI Escape Sequences

As personal computers got more sophisticated, they had better graphics and could display more colors. However, different vendors had their own idea about the API design for controlling it. That changed a few decades ago when people at the American National Standards Institute decided to unify it by defining ANSI escape codes.

Most of today’s terminal emulators support this standard to some degree. Until recently, the Windows operating system was a notable exception. Therefore, if you want the best portability, use the colorama library in Python. It translates ANSI codes to their appropriate counterparts in Windows while keeping them intact in other operating systems.

To check if your terminal understands a subset of the ANSI escape sequences, for example, related to colors, you can try using the following command:

$ tput colors

My default terminal on Linux says it can display 256 distinct colors, while xterm gives me only 8. The command would return a negative number if colors were unsupported.

ANSI escape sequences are like a markup language for the terminal. In HTML you work with tags, such as <b> or <i>, to change how elements look in the document. These tags are mixed with your content, but they’re not visible themselves. Similarly, escape codes won’t show up in the terminal as long as it recognizes them. Otherwise, they’ll appear in the literal form as if you were viewing the source of a website.

As its name implies, a sequence must begin with the non-printable Esc character, whose ASCII value is 27, sometimes denoted as 0x1b in hexadecimal or 033 in octal. You may use Python number literals to quickly verify it’s indeed the same number:

>>> 27 == 0x1b == 0o33
True

Additionally, you can obtain it with the \e escape sequence in the shell:

$ echo -e "\e"

The most common ANSI escape sequences take the following form:

The numeric code can be one or more numbers separated with a semicolon, while the character code is just one letter. Their specific meaning is defined by the ANSI standard. For example, to reset all formatting, you would type one of the following commands, which use the code zero and the letter m:

$ echo -e "\e[0m"
$ echo -e "\x1b[0m"
$ echo -e "\033[0m"

At the other end of the spectrum, you have compound code values. To set foreground and background with RGB channels, given that your terminal supports 24-bit depth, you could provide multiple numbers:

$ echo -e "\e[38;2;0;0;0m\e[48;2;255;255;255mBlack on white\e[0m"

It’s not just text color that you can set with the ANSI escape codes. You can, for example, clear and scroll the terminal window, change its background, move the cursor around, make the text blink or decorate it with an underline.

In Python, you’d probably write a helper function to allow for wrapping arbitrary codes into a sequence:

>>> def esc(code):
...     return f'\033[{code}m'
...
>>> print(esc('31;1;4') + 'really' + esc(0) + ' important')

This would make the word really appear in red, bold, and underlined font:

However, there are higher-level abstractions over ANSI escape codes, such as the mentioned colorama library, as well as tools for building user interfaces in the console.

Building Console User Interfaces

While playing with ANSI escape codes is undeniably a ton of fun, in the real world you’d rather have more abstract building blocks to put together a user interface. There are a few libraries that provide such a high level of control over the terminal, but curses seems to be the most popular choice.

C:\> pip install windows-curses

Primarily, it allows you to think in terms of independent graphical widgets instead of a blob of text. Besides, you get a lot of freedom in expressing your inner artist, because it’s really like painting a blank canvas. The library hides the complexities of having to deal with different terminals. Other than that, it has great support for keyboard events, which might be useful for writing video games.

How about making a retro snake game? Let’s create a Python snake simulator:

First, you need to import the curses module. Since it modifies the state of a running terminal, it’s important to handle errors and gracefully restore the previous state. You can do this manually, but the library comes with a convenient wrapper for your main function:

import curses

def main(screen):
    pass

if __name__ == '__main__':
    curses.wrapper(main)

Note, the function must accept a reference to the screen object, also known as stdscr, that you’ll use later for additional setup.

If you run this program now, you won’t see any effects, because it terminates immediately. However, you can add a small delay to have a sneak peek:

import time, curses

def main(screen):
    time.sleep(1)

if __name__ == '__main__':
    curses.wrapper(main)

This time the screen went completely blank for a second, but the cursor was still blinking. To hide it, just call one of the configuration functions defined in the module:

import time, curses

def main(screen):
    curses.curs_set(0)  # Hide the cursor
    time.sleep(1)

if __name__ == '__main__':
    curses.wrapper(main)

Let’s define the snake as a list of points in screen coordinates:

snake = [(0, i) for i in reversed(range(20))]

The head of the snake is always the first element in the list, whereas the tail is the last one. The initial shape of the snake is horizontal, starting from the top-left corner of the screen and facing to the right. While its y-coordinate stays at zero, its x-coordinate decreases from head to tail.

To draw the snake, you’ll start with the head and then follow with the remaining segments. Each segment carries (y, x) coordinates, so you can unpack them:

# Draw the snake
screen.addstr(*snake[0], '@')
for segment in snake[1:]:
    screen.addstr(*segment, '*')

Again, if you run this code now, it won’t display anything, because you must explicitly refresh the screen afterward:

import time, curses

def main(screen):
    curses.curs_set(0)  # Hide the cursor

    snake = [(0, i) for i in reversed(range(20))]

    # Draw the snake
    screen.addstr(*snake[0], '@')
    for segment in snake[1:]:
        screen.addstr(*segment, '*')

    screen.refresh()
    time.sleep(1)

if __name__ == '__main__':
    curses.wrapper(main)

You want to move the snake in one of four directions, which can be defined as vectors. Eventually, the direction will change in response to an arrow keystroke, so you may hook it up to the library’s key codes:

directions = {
    curses.KEY_UP: (-1, 0),
    curses.KEY_DOWN: (1, 0),
    curses.KEY_LEFT: (0, -1),
    curses.KEY_RIGHT: (0, 1),
}

direction = directions[curses.KEY_RIGHT]

How does a snake move? It turns out that only its head really moves to a new location, while all other segments shift towards it. In each step, almost all segments remain the same, except for the head and the tail. Assuming the snake isn’t growing, you can remove the tail and insert a new head at the beginning of the list:

# Move the snake
snake.pop()
snake.insert(0, tuple(map(sum, zip(snake[0], direction))))

To get the new coordinates of the head, you need to add the direction vector to it. However, adding tuples in Python results in a bigger tuple instead of the algebraic sum of the corresponding vector components. One way to fix this is by using the built-in zip(), sum(), and map() functions.

The direction will change on a keystroke, so you need to call .getch() to obtain the pressed key code. However, if the pressed key doesn’t correspond to the arrow keys defined earlier as dictionary keys, the direction won’t change:

# Change direction on arrow keystroke
direction = directions.get(screen.getch(), direction)

By default, however, .getch() is a blocking call that would prevent the snake from moving unless there was a keystroke. Therefore, you need to make the call non-blocking by adding yet another configuration:

def main(screen):
    curses.curs_set(0)    # Hide the cursor
    screen.nodelay(True)  # Don't block I/O calls

You’re almost done, but there’s just one last thing left. If you now loop this code, the snake will appear to be growing instead of moving. That’s because you have to erase the screen explicitly before each iteration.

Finally, this is all you need to play the snake game in Python:

import time, curses

def main(screen):
    curses.curs_set(0)    # Hide the cursor
    screen.nodelay(True)  # Don't block I/O calls

    directions = {
        curses.KEY_UP: (-1, 0),
        curses.KEY_DOWN: (1, 0),
        curses.KEY_LEFT: (0, -1),
        curses.KEY_RIGHT: (0, 1),
    }

    direction = directions[curses.KEY_RIGHT]
    snake = [(0, i) for i in reversed(range(20))]

    while True:
        screen.erase()

        # Draw the snake
        screen.addstr(*snake[0], '@')
        for segment in snake[1:]:
            screen.addstr(*segment, '*')

        # Move the snake
        snake.pop()
        snake.insert(0, tuple(map(sum, zip(snake[0], direction))))

        # Change direction on arrow keystroke
        direction = directions.get(screen.getch(), direction)

        screen.refresh()
        time.sleep(0.1)

if __name__ == '__main__':
    curses.wrapper(main)

This is merely scratching the surface of the possibilities that the curses module opens up. You may use it for game development like this or more business-oriented applications.

Living It Up With Cool Animations

Not only can animations make the user interface more appealing to the eye, but they also improve the overall user experience. When you provide early feedback to the user, for example, they’ll know if your program’s still working or if it’s time to kill it.

To animate text in the terminal, you have to be able to freely move the cursor around. You can do this with one of the tools mentioned previously, that is ANSI escape codes or the curses library. However, I’d like to show you an even simpler way.

If the animation can be constrained to a single line of text, then you might be interested in two special escape character sequences:

• Carriage return: \r
• Backspace: \b

The first one moves the cursor to the beginning of the line, whereas the second one moves it only one character to the left. They both work in a non-destructive way without overwriting text that’s already been written.

Let’s take a look at a few examples.

You’ll often want to display some kind of a spinning wheel to indicate a work in progress without knowing exactly how much time’s left to finish:

Many command line tools use this trick while downloading data over the network. You can make a really simple stop motion animation from a sequence of characters that will cycle in a round-robin fashion:

from itertools import cycle
from time import sleep

for frame in cycle(r'-\|/-\|/'):
    print('\r', frame, sep='', end='', flush=True)
    sleep(0.2)

The loop gets the next character to print, then moves the cursor to the beginning of the line, and overwrites whatever there was before without adding a newline. You don’t want extra space between positional arguments, so separator argument must be blank. Also, notice the use of Python’s raw strings due to backslash characters present in the literal.

When you know the remaining time or task completion percentage, then you’re able to show an animated progress bar:

First, you need to calculate how many hashtags to display and how many blank spaces to insert. Next, you erase the line and build the bar from scratch:

from time import sleep

def progress(percent=0, width=30):
    left = width * percent // 100
    right = width - left
    print('\r[', '#' * left, ' ' * right, ']',
          f' {percent:.0f}%',
          sep='', end='', flush=True)

for i in range(101):
    progress(i)
    sleep(0.1)

As before, each request for update repaints the entire line.

Making Sounds With print()

If you’re old enough to remember computers with a PC speaker, then you must also remember their distinctive beep sound, often used to indicate hardware problems. They could barely make any more noises than that, yet video games seemed so much better with it.

Today you can still take advantage of this small loudspeaker, but chances are your laptop didn’t come with one. In such a case, you can enable terminal bell emulation in your shell, so that a system warning sound is played instead.

Go ahead and type this command to see if your terminal can play a sound:

$ echo -e "\a"

This would normally print text, but the -e flag enables the interpretation of backslash escapes. As you can see, there’s a dedicated escape sequence \a, which stands for “alert”, that outputs a special bell character. Some terminals make a sound whenever they see it.

Similarly, you can print this character in Python. Perhaps in a loop to form some kind of melody. While it’s only a single note, you can still vary the length of pauses between consecutive instances. That seems like a perfect toy for Morse code playback!

The rules are the following:

• Letters are encoded with a sequence of dot (·) and dash (–) symbols.
• A dot is one unit of time.
• A dash is three units of time.
• Individual symbols in a letter are spaced one unit of time apart.
• Symbols of two adjacent letters are spaced three units of time apart.
• Symbols of two adjacent words are spaced seven units of time apart.

According to those rules, you could be “printing” an SOS signal indefinitely in the following way:

while True:
    dot()
    symbol_space()
    dot()
    symbol_space()
    dot()
    letter_space()
    dash()
    symbol_space()
    dash()
    symbol_space()
    dash()
    letter_space()
    dot()
    symbol_space()
    dot()
    symbol_space()
    dot()
    word_space()

In Python, you can implement it in merely ten lines of code:

from time import sleep

speed = 0.1

def signal(duration, symbol):
    sleep(duration)
    print(symbol, end='', flush=True)

dot = lambda: signal(speed, '·\a')
dash = lambda: signal(3*speed, '−\a')
symbol_space = lambda: signal(speed, '')
letter_space = lambda: signal(3*speed, '')
word_space = lambda: signal(7*speed, ' ')

Maybe you could even take it one step further and make a command line tool for translating text into Morse code? Either way, I hope you’re having fun with this!

Tracing

Also known as print debugging or caveman debugging, it’s the most basic form of debugging. While a little bit old-fashioned, it’s still powerful and has its uses.

The idea is to follow the path of program execution until it stops abruptly, or gives incorrect results, to identify the exact instruction with a problem. You do that by inserting print statements with words that stand out in carefully chosen places.

Take a look at this example, which manifests a rounding error:

>>> def average(numbers):
...     print('debug1:', numbers)
...     if len(numbers) > 0:
...         print('debug2:', sum(numbers))
...         return sum(numbers) / len(numbers)
...
>>> 0.1 == average(3*[0.1])
debug1: [0.1, 0.1, 0.1]
debug2: 0.30000000000000004
False

As you can see, the function doesn’t return the expected value of 0.1, but now you know it’s because the sum is a little off. Tracing the state of variables at different steps of the algorithm can give you a hint where the issue is.

This method is simple and intuitive and will work in pretty much every programming language out there. Not to mention, it’s a great exercise in the learning process.

On the other hand, once you master more advanced techniques, it’s hard to go back, because they allow you to find bugs much quicker. Tracing is a laborious manual process, which can let even more errors slip through. The build and deploy cycle takes time. Afterward, you need to remember to meticulously remove all the print() calls you made without accidentally touching the genuine ones.

Besides, it requires you to make changes in the code, which isn’t always possible. Maybe you’re debugging an application running in a remote web server or want to diagnose a problem in a post-mortem fashion. Sometimes you simply don’t have access to the standard output.

That’s precisely where logging shines.

Logging

Let’s pretend for a minute that you’re running an e-commerce website. One day, an angry customer makes a phone call complaining about a failed transaction and saying he lost his money. He claims to have tried purchasing a few items, but in the end, there was some cryptic error that prevented him from finishing that order. Yet, when he checked his bank account, the money was gone.

You apologize sincerely and make a refund, but also don’t want this to happen again in the future. How do you debug that? If only you had some trace of what happened, ideally in the form of a chronological list of events with their context.

Whenever you find yourself doing print debugging, consider turning it into permanent log messages. This may help in situations like this, when you need to analyze a problem after it happened, in an environment that you don’t have access to.

There are sophisticated tools for log aggregation and searching, but at the most basic level, you can think of logs as text files. Each line conveys detailed information about an event in your system. Usually, it won’t contain personally identifying information, though, in some cases, it may be mandated by law.

Here’s a breakdown of a typical log record:

[2019-06-14 15:18:34,517][DEBUG][root][MainThread] Customer(id=123) logged out

As you can see, it has a structured form. Apart from a descriptive message, there are a few customizable fields, which provide the context of an event. Here, you have the exact date and time, the log level, the logger name, and the thread name.

Log levels allow you to filter messages quickly to reduce noise. If you’re looking for an error, you don’t want to see all the warnings or debug messages, for example. It’s trivial to disable or enable messages at certain log levels through the configuration, without even touching the code.

With logging, you can keep your debug messages separate from the standard output. All the log messages go to the standard error stream by default, which can conveniently show up in different colors. However, you can redirect log messages to separate files, even for individual modules!

Quite commonly, misconfigured logging can lead to running out of space on the server’s disk. To prevent that, you may set up log rotation, which will keep the log files for a specified duration, such as one week, or once they hit a certain size. Nevertheless, it’s always a good practice to archive older logs. Some regulations enforce that customer data be kept for as long as five years!

Compared to other programming languages, logging in Python is simpler, because the logging module is bundled with the standard library. You just import and configure it in as little as two lines of code:

import logging
logging.basicConfig(level=logging.DEBUG)

You can call functions defined at the module level, which are hooked to the root logger, but more the common practice is to obtain a dedicated logger for each of your source files:

logging.debug('hello')  # Module-level function

logger = logging.getLogger(__name__)
logger.debug('hello')   # Logger's method

The advantage of using custom loggers is more fine-grain control. They’re usually named after the module they were defined in through the __name__ variable.

One last reason to switch from the print() function to logging is thread safety. In the upcoming section, you’ll see that the former doesn’t play well with multiple threads of execution.

Debugging

The truth is that neither tracing nor logging can be considered real debugging. To do actual debugging, you need a debugger tool, which allows you to do the following:

• Step through the code interactively.
• Set breakpoints, including conditional breakpoints.
• Introspect variables in memory.
• Evaluate custom expressions at runtime.

A crude debugger that runs in the terminal, unsurprisingly named pdb for “The Python Debugger,” is distributed as part of the standard library. This makes it always available, so it may be your only choice for performing remote debugging. Perhaps that’s a good reason to get familiar with it.

However, it doesn’t come with a graphical interface, so using pdb may be a bit tricky. If you can’t edit the code, you have to run it as a module and pass your script’s location:

$ python -m pdb my_script.py

Otherwise, you can set up a breakpoint directly in the code, which will pause the execution of your script and drop you into the debugger. The old way of doing this required two steps:

>>> import pdb
>>> pdb.set_trace()
--Return--
> <stdin>(1)<module>()->None
(Pdb)

This shows up an interactive prompt, which might look intimidating at first. However, you can still type native Python at this point to examine or modify the state of local variables. Apart from that, there’s really only a handful of debugger-specific commands that you want to use for stepping through the code.

import pdb; pdb.set_trace()

Since Python 3.7, you can also call the built-in breakpoint() function, which does the same thing, but in a more compact way and with some additional bells and whistles:

def average(numbers):
    if len(numbers) > 0:
        breakpoint()  # Python 3.7+
        return sum(numbers) / len(numbers)

You’re probably going to use a visual debugger integrated with a code editor for the most part. PyCharm has an excellent debugger, which boasts high performance, but you’ll find plenty of alternative IDEs with debuggers, both paid and free of charge.

Debugging isn’t the proverbial silver bullet. Sometimes logging or tracing will be a better solution. For example, defects that are hard to reproduce, such as race conditions, often result from temporal coupling. When you stop at a breakpoint, that little pause in program execution may mask the problem. It’s kind of like the Heisenberg principle: you can’t measure and observe a bug at the same time.

These methods aren’t mutually exclusive. They complement each other.