Resources

Here are some people that do some really cool things with the Python language itself:

• Tushar Sadhwani - GitHub (tusharsadhwani), Blog
• Frank Hoffman - GitHub (15r10nk)
• Alex Hall - GitHub (alexmojaki)
• Eric Traut - GitHub (erictraut)
• Sebastián Ramírez (@tiangolo) - GitHub (tiangolo)

The Module, Package, and Import System

https://docs.python.org/3/reference/import.html https://docs.python.org/3/tutorial/modules.html http://python-notes.curiousefficiency.org/en/latest/python_concepts/import_traps.html

Using Packages

The default package manager for Python is called "pip", and lets you install packages from the Python Package Index (PyPi).

For those with NodeJS experience, pip is similar to NPM or Yarn, and PyPi is similar to the NPM Package Repository.

Installing packages with pip is fairly straightforward, and the commands are similar to other package managers:

For the full list of commands, please see the pip reference guide.

🚨 Unlike NodeJS package managers, By default, pip installs packages globally. You can --user to scope the install to the user instead, but the best approach is to use virtual environments

Requirements Files

If you are sharing your python script, or even just using it in different environments, you probably don't want to require that its packages (dependencies) be installed one-by-one, manually with pip. A Requirements File is a way to declare all the packages required by your project, down to the specific versions, and which pip can use to provide a repeatable installation experience.

To generate a requirements file, you can either hand-code it, or use the pip freeze command:

python -m pip freeze > requirements.txt

And, then to install from it:

python -m pip install -r requirements.txt

requirements.txt vs setup.py? The usual convention is that requirements.txt is for other developers working on the project / package, whereas setup.py is for consumers of the package.

NodeJS developers: Yes, this is similar to dependencies section of package.json, although pip's package resolution algorithms are not identical to NPM or Yarn.

Working on Nested Packages Locally

Install the package(s) with an editable install:

python3 -m pip install -e {PATH}

Virtual Environments

The default behavior of pip, installing packages globally, is not very optimal once you move beyond a single project; you can easily end up with version conflicts (project A requires lib v1.0, but Project B requires lib v2.0), and other issues.

Luckily, Virtual Environments are a way in Python to have packages scoped to individual project directories, instead of the OS.

In general, if you are using Python 3.3 or newer, it is recommended to use the built-in venv module. Otherwise, the virtualenv tool (as a separate install) is the standard approach. This is a good guide that covers both. And this is a good guide for just venv. You can also find summarized step-by-step instructions for venv below.

Venv Instructions

For convenience, here are the minimal setup steps for venv (assuming you already have Python installed):

1. Navigate to directory you want mapped / controlled by venv
2. Create the virtual environment. Convention is to use either env/ or venv/ as the directory.
   • Unix / MacOS: python3 -m venv venv
   • Win: py -m venv venv

   • The folder name you use here usually (depends on shell) shows up in the CLI after activation. E.g. (venv) joshua@desktop-pc >

3. Activate the virtual environment
   • Unix / MacOS: source venv/bin/activate
   • Win: .\venv\Scripts\activate
4. Double check that Python is being loaded via the Virtual Environment
   • Unix / MacOS: which python
   • Win: where python
5. (Optional) Install dependencies, based on requirements.txt
   • python3 -m pip install -r requirements.txt

⚠ REMINDER: Don't forget to activate the environment (step 3)!

💡 Don't forget to exclude the virtual environment directory from Git / Version Control.

When you need to leave the virtual environment, run deactivate. Note that this is not destroying or removing the virtual environment directory - you are just leaving the shell shim / environment. When you want to re-enter the environment, just run the activate script again (step 3 of the above section).

Virtual Environments - Troubleshooting

Venv - Wrong Python Path

I recently found that a venv environment that I had been using for days without issue, suddenly stopped working entirely. Although it would "activate", nothing seemed to function correctly, and calls to which python returned the wrong interpreter path!

The culprit ended up being exactly what this StackOverflow answer covers - I had renamed the parent folder. If you rename the parent directory of a venv, you might need to recreate the environment from scratch!

Venv - WSL Syntax Error

When trying to activate venv on WSL, you might encounter an error like this one, about syntax errors and unexpected tokens:

source ./venv/scripts/activate
	: command not found
	-bash: ./venv/scripts/activate: line 4: syntax error near unexpected token `$'{\r''
	'bash: ./venv/scripts/activate: line 4: `deactivate () {

This is because of a line-ending issue; Python creates activate on windows with CRLF, when it should just be LF (aka \n). To fix, you can patch the file with dos2unix, with tr, or manually in an editor (just do a search and replace for \r\n with \n). I don't believe this can be done in one pass, but I might be wrong (although it would probably require some real shell skills to get as a one-liner).

Verifying the Installed Version of a Package

• pip show {package_name}

Paths

Python exposes a magic global, __file__, for each file that holds the filepath of that given file (this is analogous to __filename in NodeJS).

If you want the directory of the file, you can use a few different approaches:

file_dir = os.path.dirname(os.path.realpath(__file__))
file_dir = Path(__file__).resolve().parent

The benefit to using Path(__file__).resolve().parent is that you can more easily chain Path operations onto the end - e.g. use .parent.parent

For getting the current working directory, you can use os.getcwd():

import os
cwd = os.getcwd()

Within an interactive REPL that is launched outside of any given file, such as through the Django management shell, __file__ will be undefined, but os.getcwd() should still be available.

Strings

• Strings can be enclosed in either single or double quotes
  • Triple quotes should be used for multi-line strings (aka heredocs)
• The modern approach to string interpellation is with "f-strings".
  • E.g. f"My var = {my_var}".
  • These work with double quotes, and even triple quotes
• To join strings, you can use:
  • Plus operator ("alpha" + "bet")
  • Placing strings side-by-side ("alpha""bet")
  • The .join method
  • And more.

Regular Expressions / RegEx

🚨 WARNING: Python uses argument orders that (IMHO) are opposing to most other languages. E.g., re.sub uses re.sub(pattern, replacement, haystack) whereas most other languages use (pattern, haystack, replacement), or haystack.replace(pattern, replacement)

In general, using regular expressions is handled through the re standard library:

import re

Common use cases:

• Replace matches
  • re.sub(regex_pattern, replacement, input_string)
  • re.sub(r'(^[Hh]ello|Goodbye)', "PLEASANTRY", "Hello. How are you? Goodbye.")
  • Using back-references
    • re.sub(r".+_(.+)_sku", r"item_\1", "current_book_sku") -> 'item_book'
• Use named capture groups, extract as variables
  • match_info = re.match(regex_pattern, input_string)
  • If the pattern did not match, match_info would be None
  • You can access matched groups via:
    • Standard dict key lookup - match_info['my_group']
    • Special accessor: match_info.group('my_group')
    • Get all groups, as key-value dict: match_info.groupdict()
    • Get all group values, as tuple: match_info.groups()
  • If a sub group did not match, and had optional matching, you will get None back for that group. E.g., in using .groupdict(), you could see something like:

    {
    	"named_group_a": "abc123"
    	"named_group_b": None
    }

Classes

Syntax:

# Just used for example purposes
import datetime
from enum import Enum
from typing import Optional

class Media:
	# "class variables", which act (somewhat) like static properties
	# Shared among all class instances (*), so generally used for constants, etc.
	deprecated_physical_mediums = ["VHS", "DVD"]

	# You can also declare class properties without assigning a value
	# This is useful for strongly typing properties before assignment in `__init__` or elsewhere
	co_authors: Optional[List[str]]

	# Pretty much anything can be a class variable, including nested classes!
	class MediaType(Enum):
		Book = 1
		Movie = 2
		TV = 3
		Music = 4

	# Class constructor
	def __init__(self, title: str, author: str, published: datetime.date):
		# You can declare class properties belonging to the instance here
		self.title = title
		self.author = author
		self.published = published

	# Some methods. Note how `self` is *always* the first parameter
	def republish(self):
		self.published = datetime.date.today()

	def print_relative_age(self, date: datetime.date):
		delta = date - self.published
		print(f"{self.title} was published {delta.days} days from {date}")

	# For "getters", use `@property` decorator
	@property
	def age(self):
		return datetime.date.today() - self.published


# You can sub-class, mix-in, etc.

Classes - Init

Is defining an __init__() method always necessary? What about on subclasses?

The answer is no - neither regular classes or subclasses require an explicit __init__() method to be defined. Only declare the method if you actually want to change the behavior.

For subclasses, be aware that if you declare an __init__() method, you will need to manually call the parent init method if you want it to run; it will not be called automatically as you are overriding the method. You can use a pattern like this:

class Subclass(Superclass):
	def __init__(self):
		super(Subclass, self).__init__()
		print('Do something else')

__init__() is not a constructor. A closer parallel in Python would be __new__(), and init is called after it runs

Classes - How to Call Super

In looking at example code, you might see calls to super written as both super() and super(SubClass, self). Both are valid (in modern Python), and in Python 3+, super() is actually equivalent to the more verbose form.

Classes - How to Check For Type and Inheritance

You can use isinstance(my_instance, MyClass) to check if an instantiated instance matches a certain class type.

This should not be confused with checking if an instantiated class (the class definition itself) matches another class - for that, use issubclass(MyClass, ClassToCompareWith).

If you mix these up, you won't get an error, but will get very confusing results (will never return true)!

Dataclasses

The dataclasses module is available with Python v3.7+

Dataclasses are great for instances when you just want to wrap up a bunch of data in a nice clean, strongly-typed, class. Dataclasses cut down on boilerplate with data-holding classes as they automatically generate the __init__ method for you.

For those familiar, it can feel a lot like TypeScript interfaces + JS objects.

from dataclasses import dataclass

@dataclass
class Beverage:
	name: str
	size_in_oz: float
	iced: bool
	# You can define default values
	price: float = 3
	coupon_code: Optional[str] = None

my_beverage = Beverage('Large Iced Coffee', 32.0, True)
# kwargs work too, just like normal
my_beverage = Beverage(name='Large Iced Coffee', size_in_oz=32.0, iced=True)

There is much more to dataclasses than the above example, but often the above is all you need.

TypedDict

A TypedDict is really just a type-annotation layer over a standard dictionary.

The common way to use them is to extend the TypedDict class as a new class declaration:

from typing import TypedDict

class Beverage(TypedDict, total=True):
	name: str
	size_in_oz: float
	iced: bool

beverage: Beverage = {
	'name': 'Large Iced Coffee',
	'size_in_oz': 32.0,
	'iced': True
}

total is True by default, and is only included for illustrative purposes.

There is also an alternative syntax, which calls TypeDict:

from typing import TypedDict

Beverage = TypedDict('Beverage', {'name': str, 'size_in_oz': float, 'iced': bool}, total=True)

beverage: Beverage = {
	'name': 'Large Iced Coffee',
	'size_in_oz': 32.0,
	'iced': True
}

Note: With the alternative syntax for TypedDict declarations (type alias approach), the first argument must exactly match the variable name you are assigning to

Currently, inline / anonymous TypedDicts are not supported, although there is discussion around supporting them

With either declaration approach, you can instantiate / create / declare an instance of the typeddict through regular dictionary declaration (curly braces), or by calling the class constructor directly, with kwargs:

beverage: Beverage = {
	'name': 'Large Iced Coffee',
	'size_in_oz': 32.0,
	'iced': True
}

# or

beverage = Beverage(
	name='Large Iced Coffee',
	size_in_oz=32.0,
	iced=True
)

However, neither format supports default values for optional properties (by design).

SimpleNamespace

The SimpleNamespace object subclass is kind of an odd hybrid between a dataclass and a standard dictionary, but with less strong-typing as TypedDict. It lets you define an object instance using a single function call with keyword arguments, instead of having to define your own class.

This is kind of similar to PHP's (object) array() shorthand to convert an associative array into an object instance

Here is a basic example of a SimpleNamespace instantiation and usage

from types import SimpleNamespace

my_beverage = SimpleNamespace(
	name="Large Iced Coffee",
	size_in_oz=32.0,
	iced=True
)
# Notice that we can use dot notation here to look up values
print(my_beverage.name)

You can use SimpleNamespace to convert existing dictionaries to objects, but by default this only works one layer deep - nested dictionaries are left as-is and not converted. If you want full recursive conversion, check out the dotwiz package.

Dataclass vs TypedDict vs SimpleNamespace

• Serialization / JSON.dumps()
  • TypedDict: Serialization works out of the box (since you are still using dictionaries)
  • SimpleNamespace: Serialization pretty much works out of the box (this is basically just a dict wrapper) - just use my_simplenamespace_obj.__dict__ instead of the object directly.
    • E.g., json.dumps(my_simplenamespace_obj.__dict__)
  • dataclass: Must use dataclasses.asdict(my_dataclass) first, and there are some limitations there

• Accessor Syntax
  • TypedDict still requires that you use bracket notation to look up values, as this is really just a type annotation for a regular dictionary
    • E.g. my_typeddict["my_key"]
  • Both SimpleNamespace and dataclass let you use dot notation syntax to look up values, as they are classes
    • E.g. my_obj.my_key
• Strong static-typing
  • TypedDict: As its name implies, very strong static-typing support :)
  • SimpleNamespace: Despite the import coming from types, this has very little static-typing support. Expect no inference with current-generation tooling (mypy, pyright, etc.)
  • dataclass: Since dataclass declaration requires you to use type annotations, it has very strong static-typing support.
• Default values
  • Only dataclass: Default per-property values really only makes sense for dataclass - dictionaries cannot have have default per-property values (so the same holds for TypedDict), and although SimpleNamespace is a class, defaults don't really fit the use-case
    • WARNING: you might not want defaults anyway, considering how Python treats mutable defaults

• Docstrings

Other Special Class Types

Context Managers

• These should have an __init__, __enter__, and __exit__
• To use, call with MyContextManager as value
• You can have __init__ take values, in which case you would add parenthesis and arguments when calling with with MyContextManager

Arrays and Lists

Iteration, Enumeration, and List Comprehension

Looping through items is, generally, one of the more syntactically unique parts of Python.

List Comprehension

List comprehension is a concise (and considered pythonic) way to build new lists from existing data.

Generally, it refers to any use of the [expression for element in iterable] syntax. For example:

users = [{"user_id": i, "name": f"user_{i}"} for i in range(1,11)]
print(users[1])
# {'user_id': 2, 'name': 'user_2'}

List comprehension can get much more complex than the above example. You can nest list comprehension, combine it with filtering, and much more.

Filtering Lists in Python

List comprehension is not just for iterating; you can filter with it too:

# Using list comprehension
[my_var for my_var in my_list if my_predicate]

# Example:
mixed = ['a', None, 2, 5, 'c', None]
strings = [e for e in mixed if isinstance(e, str)]

Another option is filter(), although it should be noted that this returns an iterator, not a list:

mixed = ['a', None, 2, 5, 'c', None]
strings_iter = filter(lambda e: isinstance(e, str), mixed)

filter() also tends to have worse static typing support than plain list comprehension, at least at the moment

Fast Checking For First Match of Condition

The any() function is the best (based on speed and conciseness) approach:

# any()
has_truthy = any(my_iter)

# if needing to compute truthy on the fly
admin_assigned = any(u.is_admin for u in assigned_users)

# Lambda works too
admin_assigned = any(map(lambda u: u.is_admin == True, assigned_users))

# Above use of `any` is more readable than the `next()` approach...
first_item = next(x for x in seq if predicate(x))

# But both are much shorter and faster than...
len(list(filter())) > 0

any() is basically equivalent to lodash _.some()

If you need to actually retrieve / store the first item that matches the predicate, then use next() (and you'll often want to use a default, such as None, with it).

File Walking / Directory Traversal / File Globs

One of the easiest way to do nested file matching is with the glob package (built-in):

for jpg_file_path in glob.glob(f"{workdir}/**/*.jpg", recursive=True):
	print(f"JPG found at {jpg_file_path}")

Array / List Oddness in Python

• For joining array elements, instead of my_arr.join(separator), Python uses separator.join(my_arr)
  • Although there might be historical reasons why this was chosen, this never ceases to frustrate me.
• For filtering array elements, instead of my_arr.filter(fn), Python uses filter(fn, my_arr)
  • Example: list(filter(lambda elem: elem.startswith('b'), ['bear', 'bat', 'cat']))
• For sorting arrays, Python uses sorted(my_arr, key=optional_sort_fn)
• Python lists don't have unshift / append left functionality!
  • Quick solutions:
    • Concatenate: ['a'] + ['b', 'c']
    • Insert: my_arr.insert(0, 'a')

List Joining, Appending, and Prepending

• Adding elements to the end of a list
  • Use .append() to add a single element
  • Use .extend() to add multiple elements.
    • This can be used to join lists: list_a.extend(list_b)
• Adding elements to the beginning of a list / prepending
  • With concatenation: ['a'] + ['b', 'c']
  • With insert: my_arr.insert(0, 'a')

Advanced Indexing and Slicing

For slicing arrays, there is a short syntax that is rather useful:

a[start:stop]  # items start through stop
a[start:]      # items start through the rest of the array
a[:stop]       # items from the beginning through stop
a[:]           # a copy of the whole array
a[-n]          # item n offset from end (a[-1] would be the last element)

Use my_list[0::2] for every even element, my_list[1::2] for every odd. You can use this to extract a list of x coordinates and y coordinates if they are mixed together in a single array.

If you are working numpy arrays, such as NDArray, you have additional advanced indexing and slicing methods available to you. Some of this syntax carries over to assignment as well.

Get random list element

Since random.choice already supports structures like list, you can pass one directly to the method to get a random list element:

import random

random_pick = random.choice(my_list)

Dictionaries and Object Patterns

Looking Up Values in Dictionaries or Instances

Coming from JavaScript, one of the rudest surprises learning Python was that trying to lookup a value in a dict by a key that does not exist will throw an error, whereas in JavaScript it just return undefined. For example:

// JavaScript
const myObj = {"myKey": "myVal"};
const lookedUpValue = myObj["this_key_does_not_exist"];
console.log(lookedUpValue); // prints `undefined`

# Python
my_dict = {"myKey": "myVal"}
looked_up_val = my_dict["this_key_does_not_exist"]
print(looked_up_val) # KeyError: 'this_key_does_not_exist'

# This also applies to objects / class instances
@dataclass
class MyClass:
	my_key: str

my_instance = MyClass(my_key="myVal")
# A type-checker, like `mypy`, should also complain about
# this lookup via a property that does not exist
looked_up_val = my_instance.this_key_does_not_exist
print(looked_up_val)
# AttributeError: 'MyClass' object has no attribute 'this_key_does_not_exist'

This is a bit annoying in Python, as it means you always need to be careful about dictionary and / or object lookups. Here are some common patterns:

• Use attribute getter methods. This is my preferred option, because it allows you to specify a fallback value.
  • Dictionaries: use my_dict.get(key, OPT_fallback)
    • my_dict.get(key) is actually equivalent to my_dict.get(key, None); Python uses None as a fallback and won't throw an access error if the key does not exist
    • Use defaultdict when declaring dictionaries (more on this below)
  • Objects: Use getattr(my_obj, key, OPT_fallback)
    • Unlike my_dict.get(), for getattr if you don't specify a fallback, and try to lookup via a key / prop that does not exist, this will throw an error (AttributeError)

    # Fallback to a custom value - examples
    looked_up_val = getattr(my_obj, my_key, None)
    looked_up_val = getattr(my_obj, "user_group", "regular")

  • Using getattr(my_obj, key, None) or my_dict.get(key) makes Python act a lot more like JavaScript
    • For getattr, It even works when my_obj is None!
• Use in to check if the key or property exists first, before accessing. (the hasattr() function is a similar option, but only for non-dicts)

  if my_key in my_obj:
  	# if dictionary
  	looked_up_val = my_obj[my_key]
  	# if object / class instance
  	looked_up_val = my_obj.my_key

Another way to avoid this issue for dictionaries is to use the defaultdict class, and make sure a value is passed for the default_factory parameter.

For example:

from collections import defaultdict

my_dict = defaultdict(lambda: None)

lookedUpValue = my_dict["this_key_does_not_exist"]
print(lookedUpValue) # None

Building Dictionaries

Dictionary with a Single Dynamic Key

In JavaScript, you can construct a dictionary with a key based on a variable, like so:

let dynamicKey = getKey(); // Example val: "name"
console.log({[dynamicKey]: 'test'});
// {'name': 'test'}

In Python, the same thing can be done by just directly putting the variable name as the key:

dynamic_key = get_key() # Example val: "name"
print({dynamic_key: 'test'})
# {'name': 'test'}

Removing Dictionary Values

• To delete a prop and capture the value, use .pop()
  • my_val = my_dict.pop(my_key)
  • my_val = my_dict.pop(my_key, my_default)
• To delete a prop, use del my_dict[my_key]
  • Warning: This will throw a key error if the key does not exist in the dict
• To delete a prop that might exist, without throwing a KeyError if it doesn't, use the .pop() method with a default (None) that you ignore / discard: my_dict.pop(my_key, None)

Miscellaneous Dictionary Notes

• Checking if key exists in dict
  • if 'my_key' in my_dict
• Dictionary filling shorthand
  • In JavaScript you can do this:

  // Javascript
  const quantity = 12;
  const items = 'eggs';
  const carton = {quantity, items}

  • Unfortunately, there is no parallel in Python (not without some meta-programming magic)
• defaultdict class: Use this to instantiate a dictionary with a default value for any key that does not exist (so lookups for non-existing keys return the default value instead of throwing a KeyError)
• Deleting an object key-value pair / entry
  • Use the del keyword (e.g. del my_dict[my_key]) (acts like delete in JavaScript)
• Checking for an empty dict
  • Empty dicts are falsy, so "not empty" if my_dict else "empty" works just fine

Unpacking and Destructuring

Unpacking and Destructuring from Lists

For unpacking from an array, list, or tuple use:

drink_sizes = ['12oz', '16oz', '20oz']
tall, grande, venti = drink_sizes

If there are more elements in a list than you want to capture, you can use *my_var to capture leftovers:

tall, *_other_sizes = ['12oz', '16oz', '20oz', '24oz', '32oz']

You can unpack / spread into existing arrays as well:

drink_sizes_oz = ['12oz', '16oz', '20oz']
drink_sizes_all = ['small', 'medium', 'large', *drink_sizes_oz]

Unpacking and Destructuring from Tuples

Unpacking and destructing from tuples works the same as it does for lists.

Destructuring / unpacking while iterating

for name, age in [("Fred", 50), ("Betty", 42)]:
	print(f"{name} is {age} years old")

Unpacking and Destructuring from Dicts / Objects

Unfortunately, destructuring from dictionaries in Python requires more boilerplate code than in JS, and without shorthand syntax.

Assuming this is our sample dictionary:

# sample dict
drink_orders = {
	'fred': 'Iced Mocha',
	'jennifer': 'Americano'
}

Option A: Use itemgetter or attrgetter:

# Use `attrgetter` if class
fred_order, jennifer_order = itemgetter('fred', 'jennifer')(drink_orders)

Option B: use dict.values()

If you are using Python version 3.7 or above, this method works with any dictionary (as all dictionaries will have guaranteed order), but if you are using 3.6 or below, you will need to use an OrderedDict with this approach.

fred_order, jennifer_order = drink_orders.values()

Spreading Dicts / Objects

You can spread one dict into another with the ** (double asterisk) operator.

initial_dict = {
	'customer_name': 'Newhart',
	'item_qty': 5,
	'total': 24.32
}
updates = {
	'item_qty': 6,
	'total': 25.86
}
combined = {
	'title': 'Final Receipt',
	**initial_dict,
	**updates
}
print(combined)
# {'title': 'Final Receipt', 'customer_name': 'Newhart', 'item_qty': 6, 'total': 25.86}

This can also be used to spread a dictionary as keyword arguments (kwargs) to a function:

def greet(greeting: str, name: str):
	print(f'{greeting}, {name}')

my_args = {'name': 'Guido van Rossum', 'greeting': 'How are things'}
greet(**my_args)

However, there are multiple caveats with spreading dicts into function arguments. The main being that combining properties with the same key does not work directly with kwargs (will through got multiple values for keyword argument) and some indirection has to be used. You need to combine the values before spreading into the function arguments:

# This does NOT work
my_args = {'name': 'Guido van Rossum', 'greeting': 'How are things'}
greet(name='Joshua', **my_args)
# ^ Error: got multiple values for keyword argument 'name'

# Workaround - combine
my_args = {'name': 'Guido van Rossum', 'greeting': 'How are things'}
overrides = {'name': 'Joshua'}
greet(**{**my_args, **overrides})

Spreading a dictionary as kwargs currently has some serious issues when it comes to static type-checking, even when using TypedDict; see mypy Issue #4441 and pylance discussion #3046. This may potentially be fixed by PEP-692 and the use of Unpack.

Formatting

VSCode has an entire docs page devoted to the topic of Python linting.

The PEP8 Style Guide is a very common standard to follow when it comes to Python formatting rules and styles.

Here is a summary of some of the most popular linters and formatters.

💡 For ignoring pep8 errors, you [can add # nopep8 at the end of a line to ignore the error. Other solutions are discussed in this thread. For generally ignoring linting errors, # noqa is accepted by many linters, such as flake8. If you need to ignore issues within a giant triple-quoted string, you can add # noqa at the very end.

Formatting - Weird Edge Cases and Tips

Because Python is a language where whitespace and indenting actually alter how the program runs, there are some... interesting scenarios in which you have to be extra careful about how you format your code.

This is compounded by the fact that the recommended line length limit for pep8 is rather restrictive; just 79 characters!

Here are some special case worth mentioning (WIP):

Long IF Statements / IF with Chaining

When writing a long IF statement, and especially if it uses chaining, you will want to wrap the whole statement in parenthesis. I believe for short statements this would be anti-pythonic, but if you need to wrap due to line length, this will be necessary for parsing:

if (
	students.filter(
		degree: 'Business'
	)
	# You can leave comments mixed between chained methods this way too
	.exclude(
		drop_out_in_progress: True
	)
	.count() > 100
):
	print('Enrollment full!')

Import Statements

from mylib import Alfa, Bravo, Charlie, Delta, Echo, Foxtrot, Golf, Hotel

# To

from mylib import Alpha, Bravo, Charlie, Delta, \
	Echo, Foxtrot, Golf Hotel

Exceptions and Error Handling

Exceptions in Python can and should have distinct types.

There are a bunch of built-in exception types, such as FileNotFoundError and PermissionError, but you can also create your own by subclassing the Exception base class:

For example,

class InvalidEmailError(Exception):
	"""Raise for an invalid email address"""
	pass

if '@' not in email_input:
	raise InvalidEmailError(f'{email_input} is not a valid email')

Try / Catch With Exceptions

If you are excepting a specific exception type, you can specify:

try:
	validate_email(email)
except InvalidEmailError:
	print(f"Invalid email: {email}")

If you are unsure, you can use use a plain except clause, or capture with except Exception as my_var:

try:
	do_something()
except Exception as err
	print(err)

What happens if a thrown exception does not match any of the listed except blocks? It becomes an unhandled exception and will halt the overall execution of the program; basically the same as if you didn't have a try / except section to start with.

Profiling

Generating a pstat profile:

Using cProfile:

# cProfile
python3 -m cProfile -o myapp.profile.pstats {YOUR_NORMAL_INVOKE_STUFF}

Using Yappi (better than cProfile for multi-process / parallel operations):

# yappi
yappi --clock-type=wall --output-format=pstat -o myapp.profile.pstats {INVOKE_PATH}

Analyze (using snakeviz):

snakeviz myapp.profile.pstats

Other tools:

• pyinstrument

Crafting Python CLIs

💡 I highly recommend not using argparse. There are so many better alternatives out there, my personal favorite probably being typer which utilizes Python type-hints to speed up CLI development.

To detect when a file is being run from the CLI, use:

if __name__ == "__main__":
	...

Accessing Command Line Arguments Directly

To access command line arguments directly, use sys.argv. You'll want to skip the first one, so you often see something like:

args = sys.argv[1:]

Testing

Random PyTest Notes

📄 My blog post: Pytest Productivity – Tips I Wish I Had Known Sooner

• How to filter without allowing implicit wildcard at the end?
  • If you want to run a single test file, use pytest {FILEPATH}
  • If you want run a single test function (or method), use pytest "MODULE::MY_TEST" instead of pytest -k "SEARCH_STRING"
• List matching tests
  • Use --collect-only

How Do I...

• Write a noop? (something like the equivalent of () => {} in JS)
  • Lambda: lambda *args: None
  • Function: def my_func(*args, **kwargs):
• Ternary operator?
  • MY_VAL if CONDITION else OTHER_VAL
• Short-circuit logical-OR assignment?
  • MY_VAL = VAL_A or VAL_B
• Avoid key errors / object has no attribute errors?
  • For objects, if hasattr(object, 'my_property'), or use try / catch, or use getattr(object, 'my_property', 'fallback_value')
• Post or pre-increment?
  • You don't. Use += or -=
• Run a Python file?
  • python3 -m python_file.py
• Have an empty indent block?
  • Use pass
  • Within functions, you can also use ... (commonly used in typedefs)
• Is there an equivalent to PHP's (object) my_arr casting, to convert a Python dictionary to a class instance?
  • Yes - see SimpleNamespace.
  • If you want a utility to recursively turn a python dictionary into a dot-notation accessible class, check out the dotwiz package
• Easiest / fastest way to execute a shell command and capture the output?
  • result = subprocess.check_output(cmd_list)
    • E.g.: result = json.loads(subprocess.check_output(["node", "my_script.js", "hello"])
  • If you want success / check for zero exit code:
    • file_exists = subprocess.run(["stat", "my_file"]).returncode == 0
    • file_exists = subprocess.call(["stat", "my_file"]) == 0