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Python basics

Python was conceived in the late 1980s by Guido van Rossum. Python 3.0 final was released on December 3rd, 2008.

So please use python3 😉 #nomorepython2

Its name is based on the "Monty Python".

Python is :

an interpreted language
strongly typed and dynamically typed
- note : python can also be statically typed through type hinting
- Note: some languages are weakly typed (JavaScript), some are strongly typed (Python) and some are statically typed (Go, Rust). Being strongly typed means you can't perform operations inappropriate to the type, so for example: in Python you can't add a number typed variable with a string typed variable.
garbage collected

ZEN of python

A collection of 19 principles that influence the conception and usage of the language.

>>> import this
The Zen of Python, by Tim Peters

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!

CPython

CPython ? What is that ?

CPython is the reference implementation of the Python programming language. Written in C and Python, CPython is the default and most widely used implementation of the Python language.

CPython can be defined as both an interpreter and a compiler as it compiles Python code into bytecode before interpreting it.

-- Wikipedia

/!\ Everything in Python is an object.

Comments

Single line comments start with a # : # this is a comment

Multiline comments can be written using triple single-quotes ou double-quotes :

''' ok so this is
    a multiline comment
'''

""" and this is
    another one
    dj khaled
"""

We usually use triple-quotes to write documentation, also called docstrings.

Variables

You don't need to declare a variable before using it, nor its type.

A variable stores a piece of data, and gives it a specific name.

username = "greg"

naming

The name of a variable should use the snake_case convention.

# wrong
dictOfHashes = ['abcedf123' ...]
# right
dict_of_hashes = ['abcedf123' ...]

Reading a variable name should give you a hint about its meaning.

# wrong
a = "https://myapi.heroku.com/api/v1/register"
# right
register_url = "https://myapi.heroku.com/api/v1/register"

You should not put the type of the variable in its name.

# wrong
list_of_username = ["greg", "alex"]
# right
usernames = ["greg", "alex"]

Finally, you should NOT use reserved keywords for a variable name (eg. dict, from)

Memory management (under the hood)

TODO

https://realpython.com/pointers-in-python/

https://pythontutor.com/render.html#code=a %3D "yes" b %3D "no" l %3D [a,b] l.append(3)&cumulative=true&curInstr=4&heapPrimitives=true&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=[]&textReferences=false

primitive/native data types

the most common types are :

numbers : integers ; floating point numbers (floats) ; complex
string
list
dict
tuple
boolean (bool) : True and False
- None, 0, and empty strings/lists/dicts/tuples all evaluate to False

but there's a lot of other special ones :

iterators
range
bytes ; bytearray ; memoryview
frozenset

remember when we said that everything in python is an object ? all these types are objects, represented by a class.

>>> b = True
>>> type(b)
<class 'bool'>

more about what's a class later 😉

Note : all native types are available here

casting / converting a type to another one

a variable can be converted to another type, for example :

>>> num = "3"
>>> type(num)
<class 'str'>
>>> num = int(num)
>>> type(num)
<class 'int'>

operators

Python comes with a lot of operators that you can use and combine.

You can use them with variables of the same type, and also with variables of different types.

But be careful, some operations don't exist and will raise an Exception and crash your app.

>>> a = 1
>>> b = 2
>>> d = a / b # a division always gives a float even if the division is perfect
>>> print(type(d))
<class 'float'>
>>> print(3 // 2) # euclidean division rounds down the result of the division
1
>>> print(7 % 3) # modulo operator
1
>>> print(2 ** 6) # exponential operator (also called power)
64
>>> c = a + b
>>> c += 1 # equivalent to c = c + 1 # not all languages have this notation
>>> print(c)
4
>>> s = "username"
>>> p = "greg"
>>> print(s + ": " + p)
username: greg
>>> o = p * 3
>>> print(o)
greggreggreg
>>> l = [1,2,3]
>>> ll = l * 2
>>> print(ll)
[1, 2, 3, 1, 2, 3]
>>> lll = l + ll
>>> print(lll)
[1, 2, 3, 1, 2, 3, 1, 2, 3]
# boolean operators
>>> print(a == b)
False
>>> print(a != b)
True
>>> a = not True # negation operation
>>> print(a)
False
>>> a == b and b == c or c != d
False
# python alse let's you chain operators nicely for example for range
>>> a < b < c
True

`is` keyword

is keyword can be a little confusing and can lead to error later if not properly used.

is checks that two variables refer to the same object (memory address).

== checks if two objects have the same value.

See the difference here ?

>>> a = [1, 2, 3, 4]
>>> b = a # make `b` points to the same 'place' as `a`
>>> b is a
True
>>> b = [1, 2, 3, 4]  # re-use `b` to create a new list
>>> b is a
False # even if the two lists have the same values, they dont have the same address
>>> b == a
True

We can dig deeper with the help of the id function, which returns the memory address of an object.

>>> id(a)
4454500352
>>> id (b)
4454506560

None

None is an object. Always use is to compare a variable to None.

mutability

some sequences (tuples and strings for example) are immutable which means that you can't edit them after creation.

>>> s = "hello"
>>> s[0] = "a"
TypeError: 'str' object does not support item assignment
>>> t = (1, 2, 3)
>>> t[0] = 4
TypeError: 'tuple' object does not support item assignment

If you wan't to edit them, you need to reassign them.

>>> s = "hello"
>>> s = "a" + s[1:]
>>> print(s)
"aello"

type hinting

PEP 484

functions (intro)

quick explanation about functions, we're not going to too much into details here but we need to address the subject a little.

definition - def f()

call - f()

TODO

Signature
Arguments positionnels vs. Arguments mots clés
Attention au nommage
DRY / YAGNI
fonction vs procedure : fonction renvoie une valeur et une procédure exécute uniquement des commandes
LGI : Lorsque Python rencontre une variable, il va traiter la résolution de son nom avec des priorités particulières. D'abord il va regarder si la variable est locale, puis si elle n'existe pas localement, il vérifiera si elle est globale et enfin si elle n'est pas globale, il testera si elle est interne
def f(arg: type) -> None:
ts("obama", False, 20, True) twitter_search(username=‘@obama’, retweets=False, numtweets=20, unicode=True)
attention aux arguments par défaut

def oops(l = []):
    l.append("ok")
    return l

print(oops())
print(oops())
# ["ok"]
# ["ok", "ok"]

visualize memory here : https://pythontutor.com/render.html#code=def oops(l %3D [])%3A l.append("ok") return l print(oops()) print(oops())&cumulative=true&curInstr=11&heapPrimitives=true&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=[]&textReferences=false

built-in functions

dir() help()

classes (intro)

wait, isn't it only the beginning of the course ? sorry again, we need to address this subject too 😃

TODO

methods

TODO

magic methods

TODO

__len__

printing

TODO

f-string

TODO

Lists

A list is a sequence object that stores an ordered sequence of objects, which can be of different types (even lists, yes).

>>> alist = ["lion", 1, False, ["o", "k"]]

Each element of a list can be accessed by its index (which begins at 0).

>>> alist[0]
"lion"
>>> alist[3]
False

Trying to access an invalid index results in an Exception IndexError being raised.

>>> alist[4]
Traceback (innermost last):
  File "<stdin>", line 1, in ?
IndexError: list index out of range

One of the under-rated feature of python is its negative index ! If you want to access the last element of a list without having to calculate its size, use the -1 index !

l = [1, 2, 3]
#    0  1  2   ---> positive index
#   -3 -2 -1   ---> negative index

also works for string obviously :

+---+---+---+---+---+---+
| P | y | t | h | o | n |
+---+---+---+---+---+---+
  0   1   2   3   4   5   6
 -6  -5  -4  -3  -2  -1

Slicing

Slicing is used to get a part of a sequence. A lot of tricks can be done with slicing.

The syntax of a slice is as followed : [<start>:<stop>:<step>].

>>> l = [1, 2, 3, 4]
>>> print(l[0:3:1])
[1, 2, 3, 4]
# copy a list
>>> o = l[:] # [:] is equivalent to [0 : len(l)-1 : 1]
# create a copy of the list in reverse order
>>> r = l[::-1] # this is a real nice trick
>>> print(r)
[4, 3, 2, 1]

Common list methods

.append(<value>) adds an element at the end of the list
.pop() removes the last element of the list and returns it
.remove(<value>) removes the first occurence of a value (or raises a ValueError if the value can't be found)
.insert(<index>, <value>) adds an element at the given index
.index(<value>) returns the index of the first occurence of a value (or raises a ValueError if the value can't be found)
.sort() modify the list to sort it ascendingly
.reverse() modify the list to put it in reverse order
.count(<value>) returns the number of occurence of a value in the list

>>> l = [1,2,3]
>>> l.append(4)
>>> print(l)
[1, 2, 3, 4]
>>> l.pop()
4
>>> print(l)
[1, 2, 3]
>>> l.remove(1)
>>> print(l)
[2, 3]
>>> l.insert(0, 1)
>>> print(l)
[1, 2, 3]
>>> l.index(2)
1
>>> l + [0]
>>> print(l)
[1, 2, 3, 0]
>>> l.reverse()
>>> l
[0, 3, 2, 1]
>>> l.count(1)
1 # there is 1 occurence of the value `1`
>>> l.sort()
>>> l
[0, 1, 2, 3]

`list` built-in function

the list function converts an object to a list.

>>> s = "hello"
>>> type(s)
<class 'str'>
>>> l = list(s)
>>> l
['o', 'k']

`in` keyword

The in keyword has two purposes:

check if a value is present in a sequence (list, range, string etc.).
iterate through a sequence in a for loop

>>> l = [1, 2, 3]
# checking the presence of a value
>>> 3 in l
True
>>> 55 in l
False
# iterating
>>> for elem in l:
...     print(elem)
...
1
2
3

`len` built-in function

the len function returns the length of a sequence

>>> l = [1,2,3,4]
>>> len(l)
4

`del` keyword

use the del keyword to delete an element with its index

>>> l = [1, 2 ,3]
>>> del l[1]
>>> print(l)
[1, 3]
>>> del l[49] # deleting an index that does not exist for this list raises an Exception
IndexError: list assignment index out of range

common built-in functions used with lists

max(<list>) returns the maximum value in a list
min(<list>) returns the minimum value in a list

>>> l = [1,2,3]
>>> min(l)
1
>>> max(l)
3

strings (again!)

a string can be seen as a list of characters (because it is a sequence) ! You can access each of its characters via their index. You can also loop on it.

>>> s = "hello"
>>> print(s[0])
"h"
>>> for letter in s:
...     print(letter)
...
"h"
"e"
"l"
"l"
"o"

Two or more string literals (i.e. the ones enclosed between quotes) next to each other are automatically concatenated.

>>> 'Py' 'thon'
'Python'

This feature is particularly useful when you want to break long strings:

>>> text = ('Put several strings within parentheses '
...         'to have them joined together.')
>>> text
'Put several strings within parentheses to have them joined together.'

common string methods

<string>.join(<list>) creates a new string with each element of <list>, separated by <string>
.upper() returns the string entirely in ALL CAPS
.lower() returns the string entirely in lowercase
.capitalize() returns the string with a Uppercase at the beginning
.split(<separator>) splits the string using the separator and returns a list of string
.startswith(<value>) checks if the string starts with the value
.endswith(<value>) checks if the string ends with the value
.replace(<old>, <new>, [count=-1]) returns a copy of the string where the <old> part is replaced by the <new> part. By default, count is at -1 meaning that all occurences of <old> will be replaced. If you want only the first occurence of old to be replaced, then set count as 1
.find(<sub>) returns the first index where the sub string is found

>>> s = ".".join(["hello", "world"])
>>> s
"hello.world"
>>> s = "hello"
>>> s.upper()
"HELLO"
>>> s.lower()
"hello"
>>> s.capitalize()
"Hello"
>>> sp = "127.0.0.1"
>>> sp.split(".")
['127', '0', '0', '1']
>>> sp.startswith("127")
True
>>> sp.endswith("8")
False
>>> sp.find('0')
# string 127.0.0.1
# index  012345678
4 # index 4 ;)

ellipsis

... in python is called ellipsis. It is a string literal.

One of its main purpose is placeholding (just like pass). For example if you declare a class without attribute (like a custome Exception for example), you can use the ellipsis as a placeholder.

class CustomException:
    ...

class CustomeException:
    pass

But the no-op instruction pass is prefered and much older than the ellipsis so use pass instead 😃

tuples

Tuples are like lists but are immutable and are written with parenthesis.

>>> t = (1, 2, 3)
>>> type(t)
<class 'tuple'>
# if you want to create a tuple of one element, you need to add a comma after the element
>>> t = (1,)
>>> len(t)
1

unpacking

a sequence can be unpacked into variables.

Unpacking is powerful tool !

>>> a, b, c = (1, 2, 3)
>>> print(a, b ,c)
1 2 3
# extended unpacking is defined by an asterisk
>>> a, *b, c = (1, 2, 3, 4)
>>> print(a, b, c)
1 [2, 3] 4
# you can also use unpacking to init some variables
d, e, f = 4, 5, 6
# a neat trick with unpacking is to swap variables (commonly used in math algorithm implementation)
>>> d, e = 1, 2
>>> e, d = d, e
>>> print(d, e)
2 1

dictionaries / dict

A dict is a key/value datastructure, created by placing a sequence of elements within curly braces {}.

Keys of a dict needs to be immutable (int, float, string, tuple). But a dict is not immutable.

You can access an element using square brackets and the key. [<key>]

When the element is nested, combined multiple square brackets.

To add or edit an element, also use square brackets + the = assign operator.

>>> empty_dict = {}
>>> d = {"one": 1, "two": 2}
>>> d["one"]
1
>>> d["three"] = 3
>>> print(d)
{'one': 1, 'two': 2, 'three': 3}
>>> d["four"] = ["rowenta", "bosch"]
>>> print(d["four"][0])
"rowenta"
>>> d["unknown"] # accessing an unknown key raises a KeyError exception
KeyError: 'ok'
# in most cases, you should use the `.get()` method rather accessing directly via []

common dict methods

.keys() returns a list of all the keys
.values() returns a list of all the values
.items() returns a list of tuples which are the couples ,
.get(<key>, [default]) returns an element for a key, if the key does not exist, None is returned, except if a default is provided
.setdefault(<key>, <value>) set a default value for a key if it's not already set
.update({"four":4}) merges two dictionaries

>>> d = {"one": 1, "two": 2}
>>> d.keys()
dict_keys(['one', 'two'])
>>> d.values()
dict_values([1, 2])
>>> d.items()
dict_items([('one', 1), ('two', 2)])
>>> d.get("one")
1
>>> d.get("three")
None
>>> d.get("three", "default data 3")
"default data 3"
>>> d.setdefault("three", 3) # "three" key is not configured, so the default value is set
>>> d["three"]
3
>>> d.setdefault("three", "default 3") # "three" key is already configured
>>> d["three"]
3
>>> d.update({"four":4})
>>> d
{'one': 1, 'two': 2, 'three': 3, 'four': 4}

`in` keyword

the in keyword can be used to check if an element is a key of a dict.

>>> d = {"one": 1, "two": 2}
>>> "one" in d # is equivalent to "one" in d.keys()
True

`del` keyword

you can use the del keyword to delete a key in a dict.

trying to delete a key that doesn't exist raises a KeyError exception.

>>> d = {"one": 1, "two": 2}
>>> del d["one"]
>>> d
{'two': 2}
>>> del d["one"]
KeyError: 'one'

unpacking (again!)

Since python 3.5, you can now unpack elements in a dict, resulting in a merge or an addition !

>>> d = {'a': 1, **{'b': 2}} # addition because the key 'b' is not set
>>> print(d)
{'a': 1, 'b': 2}
>>> d = {'a': 1, **{'a': 2}} # merge because the key 'a' is already defined so its overwritten
>>> print(d)
{'a': 2}

sets

a set is a collection of unique elements. You can add multiple times the same element, but there will be only one occurence of this element in the set.

a set can be instancied with the function set or with curly braces (like a dict) {}.

a set can contain only immutable elements (strings, tuples, ints...) (like the keys of a dict).

>>> empty_set = set()
>>> s = {1,2,3,4}
>>> s.add(1) # here we try to add 1 to the set
>>> print(s)
{1, 2, 3, 4} # as we can see, 1 was already in the set so the addition didn't do anything

Like a mathematical set, we can do mathematical operations such as intersection &, union |, difference - ...

>>> s1 = {1,2,3,4}
>>> s2 = {2,3}
>>> s1 & s2 # intersection
{2, 3}
>>> s3 = {5,6}
>>> s1 | s3 # union
{1, 2, 3, 4, 5, 6}
>>> s1 - s2 # difference
{1, 4}

`in` keyword

the in keyword can be used to check if an element exists in a set, and to iterate.

>>> s1 = {1,2,3,4}
>>> 1 in s1
True
>>> 5 in s1
False
>>> for i in s1: # iterating
...     print(i)
...
1
2
3
4

pointers, pass-by-value, pass-by-reference

We need to talk about how python variables are passed between functions etc...

Python is "pass-by-object-reference"

This is equivalent to “object references are passed by value".

The variable is not the object.

Let's dig deeper. Take a look at the following examples and try to guess the answer :

>>> a = [1,2,3]
>>> b = [1,2,3]
>>> a == b # ? True or False ?
True
# remember that the `==` operator check that both sides have the same *values*
>>> a is b # ? True of False ?
False
# remember, the `is` keywork check if both sides are the same *objects* (if they have the same address)
# we can verify this claim by using the `id` function
>>> id(a) ; id(b)
4419606336
4419608320
# we can see that `a` and `b` don't have the same address

now what about this example :

>>> a = [1,2,3]
>>> b = a
>>> a == b # ? True or False ?
True
>>> a is b # ? True or False ?
True
>>> id(a) ; id(b)
4419606336
4419606336

but what if we want b to be a copy of a and not a reference to it ? 😉

we can use the .copy() method (or use a trick with slicing)

>>> a = [1,2,3]
>>> b = a.copy()
>>> a is b
False
>>> b = a[:] # remember, in slicing, the `[:]` is equal to [0 : len(<elem>)-1 : 1]

but be careful, with a "simple" copy like this, we don't clone the elements in the list, we just make new pointers to them.

if you modify the new list, elements in the old list will be affected too ! this is called a shallow copy.

>>> a = [[1,2,3],[4,5,6],[7,8,9]]
>>> b = a.copy()
>>> b
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
>>> b[0][0] = "RIP"
>>> a
[['RIP', 2, 3], [4, 5, 6], [7, 8, 9]]
>>> b
[['RIP', 2, 3], [4, 5, 6], [7, 8, 9]]

if we really want to clone the entire list, we must create a deep copy with the copy module and its function deepcopy() :

>>> import copy
>>> a = [[1,2,3],[4,5,6],[7,8,9]]
>>> b = copy.deepcopy(a)
>>> b[0][0] = "RIP"
>>> a
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
>>> b
[['RIP', 2, 3], [4, 5, 6], [7, 8, 9]]
# yay !

but what does it all mean ? well, you must remember that when you pass a list to a function, you pass the address of this list, so it can be modified by the function. And sometimes you don't want that.

Here's an example :

def reassign(l):
  l = [0, 1]

>>> l = [1,2,3]
>>> reassign(l) # here we pass the `l` variable to the function `reassign`
# what's the value of `l` now ?
# 1: [0, 1]
# 2: [1,2,3]
>>> l
[1, 2, 3] # answer 2 !
# yes we pass the list `l` by reference, but we reassign the local variable to something else

the previous example can be confusing because the local variable of the function has the same name as the the other variable (l).

we can rewrite it for more clarity :

def reassign(local_l):
  local_l = [0, 1]

>>> l = [1,2,3]
>>> reassign(l)

BUT when you don't know how python works, you can think that this list will not be modified simply because it doesn't have the same name (not true !)

so here's the final ~straw~ example 😃

def do_smth(local_l):
  local_l.append(4)

>>> l = [1,2,3]
>>> do_smth(l) # here we pass the `l` variable to the function `do_smth`
>>> print(l)
# what's the value of `l` now ?
# answer 1. [1, 2, 3, 4]
# answer 2. [1, 2, 3]
[1, 2, 3, 4]

you choosed answer 2, well done !
answer 1, yikes ! please read this chapter again 😃 (or drop an email)

source :

https://robertheaton.com/2014/02/09/pythons-pass-by-object-reference-as-explained-by-philip-k-dick/

`range` built-in function

range is a powerful function, which can be used in various ways :

generate a sequence of numbers from 0 to <stop>-1 with range(<stop>)
generate a sequence of numbers from <start> to <stop> (with an optional <step>) with range(<start>, <stop>, [step])

>>> list(range(0, 5)) # will generate a list of numbers from 0 to 4 (5-1)
[0, 1, 2, 3, 4]
>>> list(range(5, 10, 2)) # will generate a list of numbers from 5 to 9 (10-1) 2-by-2
[5, 7, 9]

(Want to see its source code ? https://github.com/python/cpython/blob/5fcfdd87c9b5066a581d3ccb4b2fede938f343ec/Objects/rangeobject.c#L76)

conditions

TODO If / elif / else break continue

loops

TODO ok

For la clause else # equivalent to a if no break Si objet séquentiel Si vous utilisez les index, cest que vous le faites surement pas bien range function enumerate function While

exercice : create a program which takes in input a number of lines, and print a pyramid.

# example of a pyramid of 4 lines
   *
  ***
 *****
*******

dict

loop over keys

for key in d:
       print(key)

loop over key and value

for k, v in d.items():
    print(f"{k} --> {v}")

cornercase avec cette façon de faire ? On ne peut pas muter un dict lorsque l’on itère dessus

d = {"ok": 0, "nok": -1}
for k in d:
    if k == "ok":
        del d[k]

préférer :

d = {"ok": 0, "nok": -1}
for k in d.keys():
    if k == 'ok':
        del d[k]

functions (again!)

`*args` and `**kwargs`

When a final formal parameter of the form **name is present, it receives a dictionary (see Mapping Types — dict) containing all keyword arguments except for those corresponding to a formal parameter. This may be combined with a formal parameter of the form *name (described in the next subsection) which receives a tuple containing the positional arguments beyond the formal parameter list. (*name must occur before **name.) For example, if we define a function like this:

def foo(bar, *args, **kwargs):
    print(bar)
    print("-" * 20)
    for arg in args:
        print(f"arg : {arg}")
    print("-" * 20)
    for k in kwargs:
        print(f"kwarg: {k} => {kwargs[k]}")

It could be called like this:

foo("a", "b", "c", k1="v1", k2="v2")

and of course it would print:

a
--------------------
arg : b
arg : c
--------------------
kwarg: k1 => v1
kwarg: k2 => v2

type annotation

Function annotations are completely optional metadata information about the types used by user-defined functions.

Annotations are stored in the annotations attribute of the function as a dictionary and have no effect on any other part of the function.

def foo(bar: str) -> str:
    print(bar)
    return "ok"

comprehension

TODO dict {k:k**2 for k in range(10)}

[i for i in range(10)]
[i for i in range(1, 20) if i % 2 == 0]
L = []

modules/packages/libraries

TODO

__name__ == "__main__"

file handling

TODO

Bonnes pratiques

TODO

PEP8

Google styling guide

Google Python Styling Guide

Documentation

TODO peut etre merge avec commentaires ? pour parler doctstring etc ?

DRY

YAGNI

Advanced

protocol

TODO

In Python a protocol is an informal interface. Protocols are either known as an accepted truth or defined in documentation and not strictly in code1. For example, any class that implements the __container__() special method is said to follow the "container protocol." While this is an accepted truth, there is no syntax in the Python language that declares a class as following a protocol. Python classes can also implement multiple protocols.

decorators

TODO

iterators

TODO

context manager

TODO

exceptions - try/except

Analogy : Imagine that you order something on the web, and during the delivery you're not at home. Error handling in this context means that the delivery company should be able to deliver your package another time/place so you can have it.

TODO

generators

TODO

OOP

TODO

functional

TODO

Design patterns

TODO

a design pattern is a general repeatable solution to a commonly occurring problem in software design. A design pattern isn't a finished design that can be transformed directly into code. It is a description or template for how to solve a problem that can be used in many different situations.

Threading

TODO

GIL

Testing

TODO

env

TODO

RESTE A FAIRE

affichage print signature d'une fonction help() function __dir__ method fstring

conditions if /elif / else == != > >= < <= continue saute à l’itération suivante break stoppe la boucle

exercice :
    jours = ["lundi", "mardi", "mercredi", "jeudi", "vendredi", "samedi", "dimanche"]
        de lundi a jeudi : travail
        vendredi : soon le w-e
        samedi dimanche : BBQ

boucle si vous utilisez des index, cest que vous le faites probablement pas bien for for i in range(len(colors)): print(colors[i])

    for i in range(len(colors)-1, -1, -1)
        print(colors[i])

    enumerate + for/else

        def find(seq, target):
            found = False
            for i, value in enumerate(seq):
                if value == target:
                    found = True
                    break
            if not found:
                return -1
            return i

        def find(seq, target):
            for i, value in enumerate(seq):
                if value == seq:
                    break
            else:
                return -1
            return i
    zip
        2 listes

    for n in range(2, 10):
        for x in range(2, n):
            if n % x == 0:
                print(n, 'equals', x, '*', n//x)
                break
        else:
            # loop fell through without finding a factor
            print(n, 'is a prime number')

valeur sentinel

    from functools import partial
    from random import randint

    pull_trigger = partial(randint, 1, 6)

    print('Starting a game of Russian Roulette...')
    print('--------------------------------------')

    for i in iter(pull_trigger, 6): #iter prend en argument une fonction ne renvo
        print('I am still alive, selected', i)
    print('Oops, game over, I am dead! :(')

    #partial() est utilisé pour une application de fonction partielle
    qui "gèle" une portion des arguments et/ou mots-clés d'une fonction
    donnant un nouvel objet avec une signature simplifiée.

while
range
iter

dir() help() - how to use it + how does it work (docstrings) sorted() - sorted(, key)

fonction (vs procedure) signature portée LGI anonyme DRY passage d'arguments renvoi de résultats arguments positionnels vs. arguments par mots clés twitter_search("@obama", False, 20, True) twitter_search("@obama", retweets=False, numtweets=20, popular=True)

tuples NamedTuples (subclass of tuple) unpacking : p = 'pommes', 30, 1 produit, quantite, prix = p

dictionnaires fundamental relationships, linking, counting, grouping

for k in d:
    print(k)

cant mutate a dict while youre iterating over it

for k in d.keys():
    if k.startswith('non'):
        del d[k]

for k in d: #non
    print(f"{k} --> {d[k]}")

for k, v in d.items():
    print(f"{k} --> {v}")

colors = ["red", "green"]
d = {}
for color in colors:
    if color not in d: #raise an error but its a not so no problem
        d[color] = 0
    d[color] += 1

d = {}
for color in colors:
    d[color] = d.get(color, 0) + 1

defaultdict # too advanded but it exists ;)

grouping :
    d = {}
    for name in names:
        key = len(name)
        d.setdefault(key, []).append(name)

    d = defaultdict(list)
    for name in names:
        key = len(name)
        d[key].append(name)

linking :
    collections.ChainMap

fichier binaire modules / package docstring

iterators

context manager with

try: #wrong way to do it
    os.remove('somefile.tmp')
except OSError:
    pass

from contextlib import suppress
with suppress(FileNotFoundError):
    os.remove('somefile.tmp')

from contextlib import contextmanager

@contextmanager
def open_db(filename: str):
    try:
        con = sqlite3.connect(filename)
        yield con
    except sqlite3.DatabaseError as err:
        logger.error(err    )
    finally:
        con.close()

one liner : 1 ligne de code = 1 phrase en francais

bonnes pratiques pep8 (syntaxe) pep257 pep20

python : everything is an object

decorateurs try except comprehension generateurs magic methods

DRY YAGNI

classes interfaces conventions (snake case, globals in all-caps) "pythonic" code https://www.python.org/dev/peps/pep-0008/ https://google.github.io/styleguide/pyguide.html

Voici quelques conseils pour vous aider à concevoir un script Python.

Réfléchissez avec un papier, un crayon... et un cerveau (voire même plusieurs) ! Reformulez avec des mots en français (ou en anglais) les consignes qui vous ont été données ou le cahier des charges qui vous a été communiqué. Dessinez ou construisez des schémas si cela vous aide.
Découpez en fonctions chaque élément de votre programme. Vous pourrez ainsi tester chaque élément indépendamment du reste. Pensez à écrire les docstrings en même temps que vous écrivez vos fonctions.
Quand l’algorithme estc omplexe, commentez votre code pour expliquer votre raisonnement. Utiliser des fonctions(ou méthodes) encore plus petites peut aussi être une solution.
Documentez-vous. L’algorithme dont vous avez besoin existe-t-il déjà dans un autre module ? Existe-t-il sous la forme de pseudo-code ? De quels outils mathématiques avez-vous besoin dans votre algorithme ?
Si vous créez ou manipulez une entité cohérente avec des propriétés propres, essayez de construire une classe.
Utilisez des noms de variables explicites, qui signifient quelquechose. En lisant votre code, on doit comprendre ce que vous faites. Choisir des noms de variables pertinents permet aussi de réduire les commentaires.
Quand vous construisez une structure de données complexe (par exemple une liste de dictionnaires contenant d’autres objets), documentez et illustrez l’organisation de cette structure de données sur un exemple simple.
Testez toujours votre code sur un jeu de données simple pour pouvoir comprendre rapidement ce qui se passe. Par exemple, une séquence de 1000 bases est plus facile à gérer que le génome humain ! Cela vous permettra également de retrouver plus facilement une erreur lorsque votre programme ne fait pas ce que vous souhaitez.
Lorsque votre programme « plante », lisez le message d’erreur. Python tente de vous expliquer ce qui ne va pas. Le numéro de la ligne qui pose problème est aussi indiqué.
Discutez avec des gens. Faites tester votre programme par d’autres. Les instructions d’utilisation sont-elles claires ?
Si vous distribuez votre code :
Rédigez une documentation claire.
Testez votre programme (jetez un œil aux tests unitaires 10).
Précisez une licence d’utilisation. Voir par exemple le site Choose an open source license

Python basics

ZEN of python

CPython

Comments

Variables

naming

Memory management (under the hood)

primitive/native data types

casting / converting a type to another one

operators

is keyword

None

mutability

type hinting

functions (intro)

built-in functions

classes (intro)

methods

magic methods

printing

f-string

Lists

Slicing

Common list methods

list built-in function

in keyword

len built-in function

del keyword

common built-in functions used with lists

strings (again!)

common string methods

ellipsis

tuples

unpacking

dictionaries / dict

common dict methods

in keyword

del keyword

unpacking (again!)

sets

in keyword

pointers, pass-by-value, pass-by-reference

range built-in function

conditions

loops

dict

functions (again!)

*args and **kwargs

type annotation

comprehension

modules/packages/libraries

file handling

Bonnes pratiques

PEP8

Google styling guide

Documentation

DRY

YAGNI

Advanced

protocol

decorators

iterators

context manager

exceptions - try/except

generators

OOP

functional

Design patterns

Threading

Testing

env

RESTE A FAIRE

`is` keyword

`list` built-in function

`in` keyword

`len` built-in function

`del` keyword

`in` keyword

`del` keyword

`in` keyword

`range` built-in function

`*args` and `**kwargs`