Starting Python 3.8, the interpreter warns about is comparison of literals.

Python 3.7:

>>> 0 is 0
True

Python 3.8:

>>> 0 is 0
<stdin>:1: SyntaxWarning: "is" with a literal. Did you mean "=="?
True

The reason is that it is an infamous Python gotcha. While == does values comparison (which is implemented by calling __eq__ magic method, in a nutshell), is compares memory addresses of objects. It's true for ints from -5 to 256 but it won't work for ints out of this range or for objects of other types:

a = -5
a is -5   # True
a = -6
a is -6   # False
a = 256
a is 256  # True
a = 257
a is 257  # False

Floating point numbers in Python and most of the modern languages are implemented according to IEEE 754. The most interesting and hardcore part is "arithmetic formats" which defines a few special values:

+ inf and -inf representing infinity.
+ nan representing a special "Not a Number" value.
+ -0.0 representing "negative zero"

Negative zero is the easiest case, for all operations it considered to be the same as the positive zero:

-.0 == .0  # True
-.0 < .0   # False

Nan returns False for all comparison operations (except !=) including comparison with inf:

import math

math.nan < 10        # False
math.nan > 10        # False
math.nan < math.inf  # False
math.nan > math.inf  # False
math.nan == math.nan # False
math.nan != 10       # True

And all binary operations on nan return nan:

math.nan + 10  # nan
1 / math.nan   # nan

You can read more about nan in previous posts:

+ https://tttttt.me/pythonetc/561
+ https://tttttt.me/pythonetc/597

Infinity is bigger than anything else (except nan). However, unlike in pure math, infinity is equal to infinity:

10 < math.inf         # True
math.inf == math.inf  # True

The sum of positive and negative infinity is nan:

-math.inf + math.inf  # nan

Infinity has an interesting behavior on division operations. Some of them are expected, some of them are surprising. Without further talking, there is a table:

truediv (/)
     |   -8 |    8 | -inf |  inf
  -8 |  1.0 | -1.0 |  0.0 | -0.0
   8 | -1.0 |  1.0 | -0.0 |  0.0
-inf |  inf | -inf |  nan |  nan
 inf | -inf |  inf |  nan |  nan

floordiv (//)
     |   -8 |    8 | -inf |  inf
  -8 |    1 |   -1 |  0.0 | -1.0
   8 |   -1 |    1 | -1.0 |  0.0
-inf |  nan |  nan |  nan |  nan
 inf |  nan |  nan |  nan |  nan

mod (%)
     |   -8 |    8 | -inf |  inf
  -8 |    0 |    0 | -8.0 |  inf
   8 |    0 |    0 | -inf |  8.0
-inf |  nan |  nan |  nan |  nan
 inf |  nan |  nan |  nan |  nan

The code used to generate the table:

import operator
cases = (-8, 8, float('-inf'), float('inf'))
ops = (operator.truediv, operator.floordiv, operator.mod)
for op in ops:
  print(op.__name__)
  row = ['{:4}'.format(x) for x in cases]
  print(' ' * 6, ' | '.join(row))
  for x in cases:
    row = ['{:4}'.format(x)]
    for y in cases:
      row.append('{:4}'.format(op(x, y)))
    print(' | '.join(row))

PEP-589 (landed in Python 3.8) introduced typing.TypedDict as a way to annotate dicts:

from typing import TypedDict

class Movie(TypedDict):
  name: str
  year: int

movie: Movie = {
  'name': 'Blade Runner',
  'year': 1982,
}

It cannot have keys that aren't explicitly specified in the type:

movie: Movie = {
  'name': 'Blade Runner',
  'year': 1982,
  'director': 'Ridley Scott',  # fails type checking
}

Also, all specified keys are required by default but it can be changed by passing total=False:

movie: Movie = {} # fails type checking

class Movie2(TypedDict, total=False):
  name: str
  year: int

movie2: Movie2 = {} # ok

PEP-526, introducing syntax for variable annotations (laded in Python 3.6), allows annotating any valid assignment target:

c.x: int = 0
c.y: int

d = {}
d['a']: int = 0
d['b']: int

The last line is the most interesting one. Adding annotations to an expression suppresses its execution:

d = {}

# fails
d[1]
# KeyError: 1

# nothing happens
d[1]: 1

Despite being a part of the PEP, it's not supported by mypy:

$ cat tmp.py
d = {}
d['a']: int
d['b']: str
reveal_type(d['a'])
reveal_type(d['b'])

$ mypy tmp.py
tmp.py:2: error: Unexpected type declaration
tmp.py:3: error: Unexpected type declaration
tmp.py:4: note: Revealed type is 'Any'
tmp.py:5: note: Revealed type is 'Any'

In most of the programming languages (like C, PHP, Go, Rust) values can be passed into a function either as value or as reference (pointer):

+ Call by value means that the value of the variable is copied, so all modification with the argument value inside the function won't affect the original value. This is an example of how it works in Go:

package main

func f(v2 int) {
  v2 = 2
  println("f v2:", v2)
  // Output: f v2: 2
}

func main() {
  v1 := 1
  f(v1)
  println("main v1:", v1)
  // Output: main v1: 1
}

+ Call by reference means that all modifications that are done by the function, including reassignment, will modify the original value:

package main

func f(v2 *int) {
  *v2 = 2
  println("f v2:", *v2)
  // Output: f v2: 2
}

func main() {
  v1 := 1
  f(&v1)
  println("main v1:", v1)
  // Output: main v1: 2
}

So, which one is used in Python? Well, neither.

In Python, the caller and the function share the same value:

def f(v2: list):
  v2.append(2)
  print('f v2:', v2)
  # f v2: [1, 2]

v1 = [1]
f(v1)
print('v1:', v1)
# v1: [1, 2]

However, the function can't replace the value (reassign the variable):

def f(v2: int):
  v2 = 2
  print('f v2:', v2)
  # f v2: 2

v1 = 1
f(v1)
print('v1:', v1)
# v1: 1

This approach is called Call by sharing. That means the argument is always passed into a function as a copy of the pointer. So, both variables point to the same boxed object in memory but if the pointer itself is modified inside the function, it doesn't affect the caller code.

What if we want to modify a collection inside a function but don't want these modifications to affect the caller code? Then we should explicitly copy the value.

For this purpose, all mutable built-in collections provide method .copy:

def f(v2):
  v2 = v2.copy()
  v2.append(2)
  print(f'{v2=}')
  # v2=[1, 2]
v1 = [1]
f(v1)
print(f'{v1=}')
# v1=[1]

Custom objects (and built-in collections too) can be copied using copy.copy:

import copy

class C:
    pass

def f(v2: C):
  v2 = copy.copy(v2)
  v2.p = 2
  print(f'{v2.p=}')
  # v2.p=2

v1 = C()
v1.p = 1
f(v1)
print(f'{v1.p=}')
# v1.p=1

However, copy.copy copies only the object itself but not underlying objects:

v1 = [[1]]
v2 = copy.copy(v1)
v2.append(2)
v2[0].append(3)
print(f'{v1=}, {v2=}')
# v1=[[1, 3]], v2=[[1, 3], 2]

So, if you need to copy all subobjects recursively, use, copy.deepcopy:

v1 = [[1]]
v2 = copy.deepcopy(v1)
v2[0].append(2)
print(f'{v1=}, {v2=}')
# v1=[[1]], v2=[[1, 2]]

Python uses eager evaluation. When a function is called, all its arguments are evaluated from left to right and only then their results are passed into the function:

print(print(1) or 2, print(3) or 4)
# 1
# 3
# 2 4

Operators and and or are lazy, the right value is evaluated only if needed (for or if the left value is falsy, and for and if the left value is truthy):

print(1) or print(2) and print(3)
# 1
# 2

For mathematical operators, the precedence is how it is in math:

1 + 2 * 3
# 7

The most interesting case is operator ** (power) which is (supposedly, the only thing in Python which is) evaluated from right to left:

2 ** 3 ** 4 == 2 ** (3 ** 4)
# True

Most of the exceptions raised from the standard library or built-ins have a quite descriptive self-contained message:

try:
  [][0]
except IndexError as e:
  exc = e

exc.args
# ('list index out of range',)

However, KeyError is different: instead of a user-friendly error message it contains the key which is missed:

try:
  {}[0]
except KeyError as e:
  exc = e

exc.args
# (0,)

So, if you log an exception as a string, make sure you save the class name (and the traceback) as well, or at least use repr instead of str:

repr(exc)
# 'KeyError(0)'

When something fails, usually you want to log it. Let's have a look at a small toy example:

from logging import getLogger

logger = getLogger(__name__)
channels = {}

def update_channel(slug, name):
  try:
    old_name = channels[slug]
  except KeyError as exc:
    logger.error(repr(exc))
  ...

update_channel('pythonetc', 'Python etc')
# Logged: KeyError('pythonetc')

This example has a few issues:

+ There is no explicit log message. So, when it fails, you can't search in the project where this log record comes from.
+ There is no traceback. When the try block execution is more complicated, we want to be able to track where exactly in the call stack the exception occurred. To achieve it, logger methods provide exc_info argument. When it is set to True, the current exception with traceback will be added to the log message.

So, this is how we can do it better:

def update_channel(slug, name):
  try:
    old_name = channels[slug]
  except KeyError as exc:
    logger.error('channel not found', exc_info=True)
  ...

update_channel('pythonetc', 'Python etc')
# channel not found
# Traceback (most recent call last):
#   File "...", line 3, in update_channel
#     old_name = channels[slug]
# KeyError: 'pythonetc'

Also, the logger provides a convenient method exception which is the same as error with exc_info=True:

logger.exception('channel not found')

Let's have a look at the following log message:

import logging
logger = logging.getLogger(__name__)
logger.warning('user not found')
# user not found

When this message is logged, it can be hard based on it alone to reproduce the given situation, to understand what went wrong. So, it's good to provide some additional context. For example:

user_id = 13
logger.warning(f'user #{user_id} not found')

That's better, now we know what user it was. However, it's hard to work with such kinds of messages. For example, we want to get a notification when the same type of error messages occurred too many times in a minute. Before, it was one error message, "user not found". Now, for every user, we get a different message. Or another example, if we want to get all messages related to the same user. If we just search for "13", we will get many false positives where "13" means something else, not user_id.

The solution is to use structured logging. The idea of structured logging is to store all additional values as separate fields instead of mixing everything in one text message. In Python, it can be achieved by passing the variables as the extra argument. Most of the logging libraries will recognize and store everything passed into extra. For example, how it looks like in python-json-logger:

from pythonjsonlogger import jsonlogger

logger = logging.getLogger()

handler = logging.StreamHandler()
formatter = jsonlogger.JsonFormatter()
handler.setFormatter(formatter)
logger.addHandler(handler)

logger.warning('user not found', extra=dict(user_id=13))
# {"message": "user not found", "user_id": 13}

However, the default formatter doesn't show extra:

logger = logging.getLogger()
logger.warning('user not found', extra=dict(user_id=13))
# user not found

So, if you use extra, stick to the third-party formatter you use or write your own.

Multiline string literal preserves every symbol between opening and closing quotes, including indentation:

def f():
  return """
    hello
      world
  """
f()
# '\n    hello\n      world\n  '

A possible solution is to remove indentation, Python will still correctly parse the code:

def f():
  return """
hello
  world
"""
f()
# '\nhello\n  world\n'

However, it's difficult to read because it looks like the literal is outside of the function body but it's not. So, a much better solution is not to break the indentation but instead remove it from the string content using textwrap.dedent:

from textwrap import dedent

def f():
  return dedent("""
    hello
      world
  """)
f()
# '\nhello\n  world\n'

If any function can modify any passed argument, how to prevent a value from modification? Make it immutable! That means the object doesn't have methods to modify it in place, only methods returning a new value. This is how numbers and str are immutable. While list has append method that modifies the object in place, str just doesn't have anything like this, all modifications return a new str:

a = b = 'ab'
a is b  # True
b += 'cd'
a is b  # False

This is why every built-in collection has an immutable version:

+ Immutable list is tuple.
+ Immutable set is frozenset.
+ Immutable bytearray is bytes.
+ dict doesn't have an immutable version but since Python 3.3 it has types.MappingProxyType wrapper that makes it immutable:

from types import MappingProxyType

orig = {1: 2}
immut = MappingProxyType(orig)

immut[3] = 4
# TypeError: 'mappingproxy' object does not support item assignment

And since it is just a proxy, not a new type, it reflects all the changes in the original mapping:

orig[3] = 4
immut[3]
# 4

Python has a built-in module sqlite3 to work with SQLite database.

import sqlite3
conn = sqlite3.connect(':memory:')
cur = conn.cursor()
cur.execute('SELECT UPPER(?)', ('hello, @pythonetc!',))
cur.fetchone()
# ('HELLO, @PYTHONETC!',)

Fun fact: for explanation what is SQL Injection the documentation links xkcd about Bobby tables instead of some smart article or Wikipedia page.

Since Python doesn't have a char type, an element of str is always str:

'@pythonetc'[0][0][0][0][0]
# '@'

This is an infinite type and you can't construct in a strictly typed language (and why would you?) because it's unclear how to construct the first instance (thing-in-itself?). For example, in Haskell:

Prelude> str = str str

<interactive>:1:7: error:
    • Occurs check: cannot construct the infinite type: t1 ~ t -> t1

Some operators in Python have special names.
Many Pythonistas know about the notorious "walrus" operator (:=), but there are less famous
ones like the diamond operator (<>) — it's similar to the "not equals" operator but written in SQL style.
The diamond operator was suggested in PEP 401 as one of
the first actions of the new leader of the language Barry Warsaw after Guido went climbing Mount Everest.
Luckily, it was just an April Fool joke and the operator was never really a part of the language.
Yet, it's still available but hidden behind the "import from future" flag.

Usually you compare for non-equality using !=:

>>> "bdfl" != "flufl"
True

But if you enable the "Barry as FLUFL" feature the behavior changes:

>>> from __future__ import barry_as_FLUFL
>>> "bdfl" != "flufl"
  File "<stdin>", line 1
    "bdfl" != "flufl"
            ^
SyntaxError: with Barry as BDFL, use '<>' instead of '!='
>>> "bdfl" <> "flufl"
True

Unfortunately, this easter egg is only working in interactive mode (REPL), but not in usual *.py scripts.

By the way, it's interesting that this feature is marked as becoming mandatory in Python 4.0.

This guest post is written by Pythonic Attacks channel.

Have you ever wondered how do relative imports work?


Im pretty sure that you've done something like that at some point:

from . import bar
from .bar import foo

It's using a special magic attribute on the module called __package__.
Lets say you have the following structure:

foo/
    __init__.py
    bar/
        __init__.py
main.py

The value of __package__ for foo/__init__.py is set to "foo", and for foo/bar/__init__.py its "foo.bar".


Note that for main.py __package__ isn't set, that's because main.py is not in a package.


So when you're doing from .bar import buz within foo/__init__.py, it simply appends "bar" to foo/__init__.py's __package__ attribute, esentially it gets translated to from foo.bar import buz.


You can actually hack __package__, e.g:

>>> __package__ = "re"
>>> from . import compile
>>> compile
<function compile at 0x10e0ee550>

by @PavelDurmanov

Modules have a magic attribute called __path__. Whenever you're doing subpackage imports, __path__ is being searched for
that submodule.

__path__ looks like a list of path strings, e.g ["foo/bar", "/path/to/location"].

So if you do from foo import bar, or import foo.bar, foo's __path__ is being searched for bar. And if found - loaded.

You can play around with __path__ to test it out.

Create simple Python module anywhere on your system:


$ tree
.
└── foo.py

$ cat foo.py
def hello():
return "hello world"

Then, run the interpreter there and do the following:
python
>>> import os
>>> os.__path__ = ["."]
>>> from os.foo import hello
>>> hello()
'hello world'

As you can see, foo is now available under os:
python
>>> os.foo
<module 'os.foo' from './foo.py'>

by @PavelDurmanov