Simeon Simeonov
-Present the Python programming language in a different way than https://docs.python.org
Avoid information overload
Use examples and interaction rather than documents and slides
Basics: About the language, the Python eco-system, types, modules, functions, scopes, decorators, string formatting
Object-oriented programming in Python: How Python "really works"
Control flow: if / for / while / try, iterators, "tactical programming" tips
A brief tour through Python's standard library
Code design and best practices: How to design your code
Python is an interpreted high-level general-purpose programming language - advanced through the Python Enhancement Proposal (PEP) process
CPython is the reference implementation of Python, written in C (alternatives: pypy, jython)
python - interpreter and interpreter shell (alternatives: ipython, bpython)
libpython
Calling C from Python: Cython, CFFI, ctypes
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- $ python
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- import this
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- # The Zen of Python, by Tim Peters
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- # Beautiful is better than ugly.
- # Explicit is better than implicit.
- # Simple is better than complex.
- # Complex is better than complicated.
- # Flat is better than nested.
- # Sparse is better than dense.
- # Readability counts.
- # Special cases aren't special enough to break the rules.
- # Although practicality beats purity.
- # Errors should never pass silently.
- # Unless explicitly silenced.
- # In the face of ambiguity, refuse the temptation to guess.
- # There should be one-- and preferably only one --obvious way to do it.
- # Although that way may not be obvious at first unless you're Dutch.
- # Now is better than never.
- # Although never is often better than *right* now.
- # If the implementation is hard to explain, it's a bad idea.
- # If the implementation is easy to explain, it may be a good idea.
- # Namespaces are one honking great idea -- let's do more of those!
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- Few built-in functions.
-https://docs.python.org/3/library/functions.html
-Python uses duck typing and has typed objects but untyped variable names.
-Type constraints are not checked at compile time; rather, operations on an object may fail, signifying that the given object is not of a suitable type. Despite being dynamically-typed, Python is strongly-typed, forbidding operations that are not well-defined (for example, adding a number to a string) rather than silently attempting to make sense of them.
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- s = 'foo' # this is a string / str, same as str('foo'), may be encoded, immutable
- b = b'foo' # bytes, same as bytes('foo', 'utf-8'), may be decoded, immutable
- i = 6 # int, same as int('6'), immutable
- f = 0.1 # float, same as float('0.1'), immutable
- b = False # bool, same as bool(0), bool(''), bool(None)... immutable / constant
- n = None # NoneType, similar to 'null' in other languages, immutable / constant
- l = [1, False, 'foo'] # list, same as list((1, False, 'foo'))
- t = (1, False, 'foo') # tuple, same as tuple([1, False, 'foo']), immutable
- d = {'foo': 1, 'bar': 8} # dict, same as dict(foo=1, bar=8), similar to hash in other languages
- s = {'foo', 'bar', 1, 1, 4} # set, same as set(['foo', 'bar', 1, 1, 4]), removes duplicates
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- Classes, functions, modules, .... and even types are simply other types :)
-A module is a file containing Python definitions and statements. The file name is the module name with the suffix .py appended. Within a module, the module's name (as a string) is available as the value of the global variable __name__
-When a module named foo is imported, the interpreter first searches for a built-in module with that name (sys.builtin_module_names). If not found, it then searches for a file named foo.py in a list of directories given by the variable sys.path. sys.path is initialized from these locations:
-The module is then imported only once and "cached" in sys.modules
-Packages are a way of structuring Python's module namespace by using "dotted module names"
-The import statement combines two operations:
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- # bar.py then bar/__init__.py will be considered, the first match executed and bound to 'bar'
- import bar
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- import mymodule.foo # implicitly executes mymodule.py, mymodule/__init__.py and mymodule/foo/__init__.py
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- import numpy as np # will be bound as 'np' instead of 'numpy'. N.B. __name__ is still 'numpy'
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- import some.extremely.deep.path.Animal as Animal # "sacrifice" the namespace in the name of convinience
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- from sys import path # execute sys and only import the 'path' attribute into local scope as 'path'
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- # relative imports must be explicit in Python 3
- from .othermodule import something # expects that current module and 'othermodule' are in the same
- # package (containing __init__.py)
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- from sys import * # NO! Bad programming practice since 1879
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- Python's official package repository is PyPi (https://pypi.org), while Python's official package installer is pip (https://pypi.org/project/pip/)
-A Python environment is the physical and logical arrangement of Python modules and packages. Several options exist:
-Desired qualities for a flexible Python environment:
-Exploting the operating system can be done by:
-Using venv can be done by directly invoking python:
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- # create a virtual environment
- python -m venv my_virtual_env
- python -m venv --system-site-packages my_virtual_env
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- # load, use and unload the virtual environment
- source my_virtual_env/bin/activate
- pip install sqlalchemy
- # install package from a custom repository (https://artifactory.fifty.eu)
- pip install --index-url=https://artifactory.fifty.eu/artifactory/api/pypi/pypi/simple/ odin-data-access
- deactivate
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- # one can alternatively use the python "wrapper" of the virtual env
- my_virtual_env/bin/python -m pip install sqlalchemy
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- --system-site-packages will keep the original site-packages folders at the end of sys.path
-Poetry (https://python-poetry.org) is the prefered environment and dependency management tool at Statnett.
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- # create project and a virtual environment from scratch
- poetry new my-project
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- # ... or use Poetry with an existing one
- cd my-project
- poetry init
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- # edit pyproject.toml for your needs (f.i. add dependencies, metadata ... etc),
- # create virtual environment and install dependencies
- poetry install
- # finally commit your poetry.lock file to version control
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- # update all dependencies
- poetry update
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- For more info: https://python-poetry.org/docs/basic-usage/
-Immutable object is an object with a fixed value. Immutable objects include bool, int, float, str, bytes and tuples. Such an object cannot be altered. A new object has to be created if a different value has to be stored. They play an important role in places where a constant hash value is needed, for example as a key in a dictionary.
-All objects that are not immutable are... mutable. All hashable objects should be immutable or use id().
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- i = 1
- id(i) # returns f.i. 9788992
- i += 1 # same as i = i + 1
- id(i) # returns a different value, hence - a brand new object
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- s = 'Hello'
- s += ' World' # s is now a different object
- s[0] # 'H'
- s[0] = 'h' # TypeError: 'str' object does not support item assignment
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- t = (1, 4) # tuple
- l = [1, 4] # list
- hash(t) # returns f.i. -6333845781340707986
- hash(l) # TypeError: unhashable type: 'list'
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- s = 'the long and winding road'
- s2 = 'the long and winding road'
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- # check if s and s2 are the same object:
- id(s) # Out: 139858905258704
- id(s2) # Out: 139858926037472
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- # the hash should be the same
- hash(s) # Out: 7030216208569256362
- hash(s2) # Out: 7030216208569256362
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- A function is a sequence of program instructions that performs a specific task, packaged as a unit.
-Functions let you:
-Function calls bring some overhead pushing / popping function-data into / from stack.
-Important definitions (may have different meanings in different programming languages):
-The keyword def introduces a function definition.
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- def add(a, b): # - function definition / header
- """Function for adding integers""" # - docstring
- result = a + b
- a = 5
- return result # - function that does not contain return, implicitly returns None
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- a_param = 9
- b_param = -2
- add(a_param, b_param) # Out: 7
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- # integers are immutable and a_param will remain unchanged
- print(a_param) # Out: 9
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- def addl(a, b):
- """Function for adding two lists"""
- result = a + b
- a += [5] # in this case equal to: a.append(5)
- return result
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- a_param = [9]
- b_param = [-2]
- addl(a_param, b_param) # Out: [9, -2]
- # lists are mutable and a_param will be changed
- print(a_param) # Out: [9, 5]
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- def add(a, b):
- """Function for adding integers"""
- return a + b
- my_result = add(2, 5) # positional arguments (parameters)
- my_result = add(b=5, a=2) # keyword arguments (parameters)
- my_tuple = (2, 5)
- my_dict = {'b': 5, 'a': 2}
- my_result = add(*my_tuple) # unpacked and assigned to the positional arguments
- my_result = add(**my_dict) # unpacked and assigned to the kw. arguments
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- def add(a, b=5):
- """Function for adding integers"""
- return a + b
- my_result = add(2)
- # ... and the rest of the examples above will work
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- def add(a, *args, **kwargs):
- """Function for adding integers"""
- if args:
- b = args[0]
- elif 'b' in kwargs:
- b = kwargs['b']
- return a + b
- my_result = add(2, 5, 9, 11) # 5 assigned to args[0]
- my_result = add(2, b=5, c=9, d=11) # 5 assigned to kwargs['b']
- my_result = add(2, b=5, 9, 11) # SyntaxError: positional argument follows keyword argument
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- def decrypt(password: str, edata: str) -> str:
- """
- Decrypts `edata` using `password`.
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- `edata` is in the following format:
- enc-val$`version-num`$`bas64-salt`$`base64-encrypted_data`
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- :param password: The password to generate the key with
- :type password: str
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- :param edata: The data to be decrypted
- :type edata: str
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- :raises EtoolkitInstanceError: If the encryption format is unsupported
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- :return: The output string / decrypted data
- :rtype: str
- """
- if not edata.startswith('enc-val$1$'):
- raise EtoolkitInstanceError('Unsupported encryption format')
- # some more code magic coming after....
- # ...
- # ..
- return decrypted_str
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- import typing
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- Basestring = typing.Union[str, bytes]
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- def decrypt(password: Basestring, edata: str) -> str:
- pass
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- from typing import Union
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- def decrypt(password: Union[str, bytes], edata: str) -> str:
- """Generic documentation. No need for pass"""
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- # Python >= 3.10 only
- def decrypt(password: str | bytes, edata: str) -> str:
- """Generic documentation. No need for pass"""
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- def fetch_the_first_letter(input_str: str) -> str:
- """Fetches the first letter of the string input_str or 'x'""""
- try:
- return input_str[0]
- except Exception:
- return 'x'
- letter_list = list(map(fetch_the_first_letter, ['foo', 'bar', 'test'])) # Out: ['f', 'b', 't']
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- Small anonymous functions can be created with the lambda keyword
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- letter_list = list(map(lambda x: x[0], ['foo', 'bar', 'test'])) # Out: ['f', 'b', 't']
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- Functions in Python are callable objects. Callable objects can be created by defining the __call__ method. More on that later in the course...
-A function can return multiple values by implicitly returning a tuple:
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- def square_cube(x):
- """returns x, x^2 and x^3"""
- return x, x**2, x**3
- numbers = square_cube(5) # Out: (5, 25, 125)
- num, sqnum, cbnum = square_cube(5) # unpacking the tuple
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- Can be used as:
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- def get_multiplier_of(base: int) -> str:
- """the function enclosing its nested functions"""
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- def multiplier_function(x):
- """a nested function"""
- return base * x
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- return multiplier_function
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- times3 = get_multiplier_of(3)
- times5 = get_multiplier_of(5)
- print(times3(3)) # Out: 9
- print(times5(3)) # Out: 15
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- locals() and globals() return dicts of symbols for their respective scopes
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- num1, num2 = 7, 8 # module globals
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- def print_numbers():
- print(num1, num2) # OK, these are module globals
- num3 = 100
- print(num3) # prints 100, num3 is in the function (local) scope
- global num4 # assignes / references num4 to / in the global scope
- num4 = 99
- id = 200 # new symbol in local scope
- # id(num4) # will not yield the expected result (raises TypeError)
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- def print_numbers2():
- print(num3) # OK, enclosed scope
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- print_numbers2() # prints 100
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- print_numbers()
- # print(num3) # Raises NameError - why?
- print(num4) # Prints 99 - why?
- # print_numbers2() # Raises NameError
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- Decorators can be used to modify the behavior of the objects they decorate. Decorators can be implemented either by using classes or by using nested functions.
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- def my_decorator(func):
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- def decorated():
- print('Doing something before the decorated function')
- retval = func()
- print('Doing something after the decorated function')
- return retval
- return decorated
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- def my_function():
- print('Alice')
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- my_function = my_decorator(my_function)
- my_function()
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- ... may be dificult to read / understand, while:
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- @my_decorator
- def my_function():
- print('Alice')
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- my_function()
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- ... may be easier
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- import sys
- from functools import wraps
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- def requires_access(access_secret: str):
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- def api_access_decorator(f):
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- @wraps(f)
- def decorated(*args, **kwargs):
- if 'secret' not in kwargs:
- sys.exit('No secret provided')
- if not kwargs['secret'] or kwargs['secret'] != access_secret:
- sys.exit("Secret doesn't match")
- # return f(args[0], **kwargs)
- return f(*args, **kwargs)
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- return decorated
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- return api_access_decorator
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- @requires_access(access_secret='b28cfeaa65b73cf')
- def sensitive_function(data, **kwargs):
- """very sensitive function"""
- db.save(data)
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- f = 6.57865
- i = 27
- s = 'another string'
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- '%s - %d - %5.2f' % (s, i, f) # Out: 'another string - 27 - 6.58'
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- # still used in:
- logger.debug("%d - %s", event.id, message)
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- '{} - {} - {:5.2f}'.format(s, i, f) # implicit
- '{0} - {1} - {2:5.2f}'.format(s, i, f) # explicit
- '{my_str} - {i} - {fl:5.2f}'.format(my_str=s, fl=f, i=i) # keyword
- # Out: 'another string - 27 - 6.58'
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- # modern Python >= 3.6 f-strings
- f'{s} - {i} - {f:5.2f}' # Out: 'another string - 27 - 6.58'
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- See https://docs.python.org/3/library/string.html#formatspec for the complete format specification
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