Python
En este sitio se ofrecen muchos curso (varios de Python): https://www.coursera.org/
Este curso creo que está bueno: https://en.wikibooks.org/wiki/Non-Programmer%27s_Tutorial_for_Python_3
https://openclassrooms.com/courses/apprenez-a-programmer-en-python/les-structures-conditionnelles
http://www.enlistq.com/10-python-idioms-to-help-you-improve-your-code/
https://www.linkedin.com/pulse/python-top-10-programming-languages-year-2017-alok-kumar
Contents
- 1 Qu'est-ce que Python? - Pourquoi Python? - À quoi peut servir Python?
- 2 Instalación
- 3 Lancer Python
- 4 Premiers pas avec l'interpréteur de commandes Python
- 5 Les types de données
- 6 Les mots-clés and, or et not
- 7 Les opérateurs
- 7.1 « + » « - » « / »
- 7.2 « // » permet d'obtenir la partie entière d'une division (cociente)
- 7.3 « % », que l'on appelle le « modulo », permet de connaître le reste de la division (resto)
- 7.4 « += » « -= » « *= » « /= »
- 7.5 Potencia
- 7.6 Valor absoluto
- 7.7 Complejos
- 7.8 Maximo y minimo
- 7.9 Redondeo
- 7.10 Permutation
- 7.11 Asignar un mismo valor a varias variables
- 7.12 Couper une instruction Python, pour l'écrire sur deux lignes ou plus
- 7.13 Les opérateurs de comparaison
- 8 Funciones más utilizadas
- 9 Les structures conditionnelles
- 10 Definir una función
- 11 Exceptions
- 12 Input and Output
- 13 OOP with Python
- 14 Python Web Development
- 15 Sublime Text
Qu'est-ce que Python? - Pourquoi Python? - À quoi peut servir Python?
- Python est un langage de programmation interprété, à ne pas confondre avec un langage compilé.
- Il permet de créer toutes sortes de programmes, comme des jeux, des logiciels, des progiciels, etc.
- Il est possible d'associer des bibliothèques à Python afin d'étendre ses possibilités.
- Il est portable, c'est à dire qu'il peut fonctionner sous différents systèmes d'exploitation (Windows, Linux, Mac OS X,…).
Instalación
Python est pré-installé sur la plupart des distributions Linux. Sinon:
Versión instalada
Para ver la versión por defecto:
python --version
O simplemente entrando a la línea de comandos python a través de:
python
Ahora, en un SO pueden haber más de una versión instalada. Para ver que versiones de python se encuentran ya instaladas en nuestro sistema operativo podemos ir al directorio /usr/bin y ver que ejecutables de python se encuentran:
ls /usr/bin/python python python2 python2.7 python3 python3.5 python3.5m python3m pythontex pythontex3
y para ver la versión exacta (Python 3.5.2) ejecutamos python3.5 y este nos muestra la versión exacta al entrar a la línea de comandos python:
python3.5 Python 3.5.2 (default, Nov 17 2016, 17:05:23) [GCC 5.4.0 20160609] on linux Type "help", "copyright", "credits" or "license" for more information. >>>
Cambiar la versión por defecto
https://linuxconfig.org/how-to-change-from-default-to-alternative-python-version-on-debian-linux
Change python version on per user basis
To change a python version on per user basis you simply create an alias within user's home directory. Open ~/.bashrc file and add new alias to change your default python executable:
alias python='/usr/bin/python3.4'
Once you make the above change, re-login or source your .bashrc file:
. ~/.bashrc
Change python version system-wide
To change python version system-wide we can use update-alternatives command.
Logged in as a root user. First we can list all available python alternatives:
# update-alternatives --list python update-alternatives: error: no alternatives for python
El comando anterio debería mostrar las alternativas (por ejemplo python2.7 , python3.5) que ya han sido incluidas a través de comando update-alternatives --install. The above error message means that no python alternatives has been recognized by update-alternatives command. For this reason we need to update our alternatives table and include both python2.7 and python3.5:
Debemos entonces contruir la tabla de alternativas de la siguiente forma:
# update-alternatives --install /usr/bin/python python /usr/bin/python2.7 1 update-alternatives: using /usr/bin/python2.7 to provide /usr/bin/python (python) in auto mode # update-alternatives --install /usr/bin/python python /usr/bin/python3.5 2 update-alternatives: using /usr/bin/python3.4 to provide /usr/bin/python (python) in auto mode
The --install option take multiple arguments from which it will be able to create a symbolic link. The last argument specified it priority means, if no manual alternative selection is made the alternative with the highest priority number will be set. In our case we have set a priority 2 for /usr/bin/python3.4 and as a result the /usr/bin/python3.5 was set as default python version automatically by update-alternatives command.
Es decir, si queremos que python7 sea la versión por defecto podemos ejecutar el comando update-alternatives --intall de la forma mostrada arriba y ajustar el último argumento de forma tal que le mayor valor sea asociado a la versión que queremos por defecto.
Ubuntu
apt-get install python3.x
Si se descarga:
- Décompressez l'archive en tapant : tar -xzf Python-3.4.0.tar.bz2 (cette commande est bien entendu à adapter suivant la version et le type de compression).
- Attendez quelques instants que la décompression se termine, puis rendez-vous dans le dossier qui vient d'être créé dans le répertoire courant (Python-3.4.0 dans mon cas).
- Exécutez le script configure en tapant ./configure dans la console.
- Une fois que la configuration s'est déroulée, il n'y a plus qu'à compiler en tapant make puis make install en tant que super-utilisateur.
IDE for Python
Python en eclipse
Para utilizar Python en Eclipse debemos instalar PyDev:
Help > Eclipse Marketplace Find: PyDev
PyCharm
Instalación: https://itsfoss.com/install-pycharm-ubuntu/
Lancer Python
Lorsque vous l'avez installé sur votre système, Python a créé un lien vers l'interpréteur sous la forme python3.X (le X étant le numéro de la version installée).
Si, par exemple, vous avez installé Python 3.4, vous pouvez y accéder grâce à la commande
python3.4
Premiers pas avec l'interpréteur de commandes Python
Opérations courantes
>>> 3 + 4 7
>>> 9.5 + 2 11.5
>>> 3.11 + 2.08 5.1899999999999995
Les types de données
Para saber el tipo de una variable podemos usar la función type
>>> b='s' >>> type(b) <class 'str'>
int: Les nombres entiers
>>> i=20 >>> type(i) <class 'int'>
Le type entier se nomme int en Python (qui correspond à l'anglais « integer », c'est-à-dire entier). La forme d'un entier est un nombre sans virgule.
float: Les nombres flottants
>>> f=3.5 >>> type(f) <class 'float'>
Les flottants sont les nombres à virgule. Ils se nomment float en Python (ce qui signifie « flottant » en anglais). La syntaxe d'un nombre flottant est celle d'un nombre à virgule (n'oubliez pas de remplacer la virgule par un point). Si ce nombre n'a pas de partie flottante mais que vous voulez qu'il soit considéré par le système comme un flottant, vous pouvez lui ajouter une partie flottante de 0 (exemple 52.0).
str: Les chaînes de caractères
>>> a='c' >>> type(a) <class 'str'>
>>> s='esta es una cadena de caracteres' >>> type(s) <class 'str'>
On peut écrire une chaîne de caractères de différentes façons :
entre guillemets ("ceci est une chaîne de caractères") ;
entre apostrophes ('ceci est une chaîne de caractères') ;
entre triples guillemets ("""ceci est une chaîne de caractères""").
entre triples apostrophes ('''ceci est une chaîne de caractères''').
Si vous utilisez les délimiteurs simples (le guillemet ou l'apostrophe) pour encadrer une chaîne de caractères, il faut échapper les apostrophes se trouvant au cœur de la chaîne. On insère ainsi un caractère anti-slash « \ » avant les apostrophes contenues dans le message.
chaine = 'J\'aime le Python!'
Le caractère d'échappement « \ » est utilisé pour créer d'autres signes très utiles. Ainsi, « \n » symbolise un saut de ligne ("essai\nsur\nplusieurs\nlignes"). Pour écrire un véritable anti-slash dans une chaîne, il faut l'échapper lui-même (et donc écrire « \\ »).
L'interpréteur affiche les sauts de lignes comme on les saisit, c'est-à-dire sous forme de « \n ». Nous verrons dans la partie suivante comment afficher réellement ces chaînes de caractères et pourquoi l'interpréteur ne les affiche pas comme il le devrait.
Utiliser les triples guillemets pour encadrer une chaîne de caractères dispense d'échapper les guillemets et apostrophes, et permet d'écrire plusieurs lignes sans symboliser les retours à la ligne au moyen de « \n ».
>>> chaine3 = """Ceci est un nouvel ... essai sur plusieurs ... lignes"""
Vous pouvez utiliser, à la place des trois guillemets, trois apostrophes qui jouent exactement le même rôle.
bool: Les booléens
Les variables de ce type ne peuvent prendre comme valeur que True ou False.
>>> b=True >>> type(b) <class 'bool'>
>>> p=False >>> type(p) <class 'bool'>
>>> a = 0 >>> a == 5 False >>> a > -8 True >>> a != 33.19 True
>>> age = 21 >>> majeur = False >>> if age >= 18: >>> majeur = True
list
>>> lista=['Maracay','Los Teques','Caracas','Lyon','Dublin'] >>> type(lista) <class 'list'>
Les mots-clés and, or et not
if a>=2 and a<=8:
print("a est dans l'intervalle.")
else:
print("a n'est pas dans l'intervalle.")
if a<2 or a>8:
print("a n'est pas dans l'intervalle.")
else:
print("a est dans l'intervalle.")
Enfin, il existe le mot clé not qui « inverse » un prédicat. Le prédicat not a==5 équivaut donc à a!=5.
not rend la syntaxe plus claire. Pour cet exemple, j'ajoute à la liste un nouveau mot clé, is, qui teste l'égalité non pas des valeurs de deux variables, mais de leurs références. Je ne vais pas rentrer dans le détail de ce mécanisme avant longtemps. Il vous suffit de savoir que pour les entiers, les flottants et les booléens, c'est strictement la même chose. Mais pour tester une égalité entre variables dont le type est plus complexe, préférez l'opérateur « == ». Revenons à cette démonstration :
>>> majeur = False
>>> if majeur is not True:
... print("Vous n'êtes pas encore majeur.")
...
Vous n'êtes pas encore majeur.
>>>
Les opérateurs
« + » « - » « / »
>>> 3 + 4 7
>>> -2 + 93 91
>>> 9.5 + 2 11.5
>>> 3.11 + 2.08 5.1899999999999995
>>> 10 / 5 2.0
>>> 10 / 3 3.3333333333333335
« // » permet d'obtenir la partie entière d'une division (cociente)
>>> 10 // 3 3 >>> 10 // 4 2
« % », que l'on appelle le « modulo », permet de connaître le reste de la division (resto)
>>> 10%3 1
« += » « -= » « *= » « /= »
variable = variable + 1
La operación anterior puede resumirse utilizando el operador +=:
variable += 1
Les opérateurs -=, *= et /= existent également, bien qu'ils soient moins utilisés.
Potencia
>>> 9**(1/2) 3.0
>>> pow(3,2) 9
Valor absoluto
>>> abs(-3.0) 3.0
Complejos
>>> complex(2,3) (2+3j)
>>> complex(2,3)*complex(3,4) (-6+17j)
>>> a=complex(3,5) >>> a.real 3.0 >>> a.imag 5.0
Maximo y minimo
>>> max(3,45,6,7) 45
>>> min(32,23,2,13,4.3) 2
Redondeo
>>> round(34.5) 35.0
>>> round(35.345,1) 35.3
Permutation
Python propose un moyen simple de permuter deux variables (échanger leur valeur). Dans d'autres langages, il est nécessaire de passer par une troisième variable qui retient l'une des deux valeurs… ici c'est bien plus simple :
>>> a = 5 >>> b = 32 >>> a,b = b,a # permutation >>> a 32 >>> b 5 >>>
Asignar un mismo valor a varias variables
On peut aussi affecter assez simplement une même valeur à plusieurs variables :
>>> x = y = 3 >>> x 3 >>> y 3
Couper une instruction Python, pour l'écrire sur deux lignes ou plus
>>> 1 + 4 - 3 * 19 + 33 - 45 * 2 + (8 - 3) \ ... -6 + 23.5 -86.5 >>>
Les opérateurs de comparaison
< Strictement inférieur à > Strictement supérieur à <= Inférieur ou égal à >= Supérieur ou égal à == Égal à != Différent de
Funciones más utilizadas
Une fonction exécute un certain nombre d'instructions déjà enregistrées. En gros, c'est comme si vous enregistriez un groupe d'instructions pour faire une action précise et que vous lui donniez un nom. Vous n'avez plus ensuite qu'à appeler cette fonction par son nom autant de fois que nécessaire.
La plupart des fonctions ont besoin d'au moins un paramètre pour travailler sur une donnée ; ces paramètres sont des informations que vous passez à la fonction afin qu'elle travaille dessus.
Les fonctions s'utilisent en respectant la syntaxe suivante :
nom_de_la_fonction(parametre_1,parametre_2,…,parametre_n)
type
Une des grandes puissances de Python est qu'il comprend automatiquement de quel type est une variable et cela lors de son affectation. Mais il est pratique de pouvoir savoir de quel type est une variable.
La syntaxe de cette fonction est simple. Demanda el nombre de la variable o directamente una variable :
type(variable) type(nom_de_la_variable)
La fonction renvoie le type de la variable passée en paramètre.
>>> type(3) <class 'int'>
>>> type(True) <class 'bool'>
La clase math
>>> import math
Raiz cuadrada
>>> math.sqrt(2) 1.4142135623730951
Factorial
>>> math.factorial(5) 120
PI
>>> math.pi 3.1415926535897931
Funciones trigonométricas
>>> math.sin(math.pi) 1.2246063538223773e-16
Logaritmos
>>> math.log(11) 2.3978952727983707
Les structures conditionnelles
IF / ELSE / ELIF
>>> a = 5
>>> if a > 0:
... print("a est supérieur à 0.") # Si no se coloca la identation dentro del if, se genera un error: IndentationError: expected an indented block
...
a est supérieur à 0.
>>> age = 21
>>> if age >= 18:
... print("Vous êtes majeur.")
... else:
... print("Vous êtes mineur.")
...
Vous êtes majeur.
>>> a=5
>>> if a > 0: # Positif
... print("a est positif.")
... elif a < 0:
... print("a est négatif.")
... else:
... print("a est nul.")
...
>>> a est positif.
Definir una función
def hello():
print("Hello")
def area(width, height):
return width * height
def print_welcome(name):
print("Welcome", name)
hello()
hello()
print_welcome("Fred")
w = 4
h = 5
print("width =", w, " height =", h, " area =", area(w, h))
def factorial():
n=int(input("Entrez un entier positif: "))
fac=1
for i in range(n,1,-1):
fac=fac*i
return(fac)
print(factorial())
Exceptions
while True:
try:
x = int(raw_input("Please enter a number: "))
break
except ValueError:
print "Oops! That was no valid number. Try again..."Input and Output
Input and Output funtions: Esta página creo que no está actualizada para python 3, pero está bien organizada: https://en.wikibooks.org/wiki/Python_Programming/Input_and_Output#input.28.29
input
name = input("What's your name? ")
print("Nice to meet you " + name + "!")
age = input("Your age? ")
print("So, you are already " + age + " years old, " + name + "!")
La función input asigna por defecto una variable de tipo str. Si queremos que la variable sea tipo int o list:
>>> population = int(input("Population of Toronto? "))
Population of Toronto? 2615069
>>> print(population, type(population))
2615069 <class 'int'>
>>> cities_canada = eval(input("Largest cities in Canada: "))
Largest cities in Canada: ["Toronto", "Montreal", "Calgara", "Ottawa"]
>>> print(cities_canada, type(cities_canada))
['Toronto', 'Montreal', 'Calgara', 'Ottawa'] <class 'list'>
output
>>> a=3
>>> b=4
// Las variables int deben ser delimitadas entre comas:
>>> print("a =",a,"et b =",b)
a = 6 et b = 4
>>> a='3'
>>> b='4'
// Las variables str pueden ser delimitadas entre comas o entre +:
>>> print("a = "+a+" et b = "+b)
a = 6 et b = 4
>>> print("Hello World !")
Hello World !
OOP with Python
https://python.swaroopch.com/oop.html
Algunos conceptos generales sobre Object Oriented Programming
In all the programs we wrote till now, we have designed our program around functions i.e. blocks of statements which manipulate data. This is called the procedure-oriented way of programming. There is another way of organizing your program which is to combine data and functionality and wrap it inside something called an object. This is called the object oriented programming paradigm.
Classes and objects are the two main aspects of object oriented programming. A class creates a new type where objects are instances of the class. An analogy is that you can have variables of type int which translates to saying that variables that store integers are variables which are instances (objects) of the int class.
Note for Static Language Programmers:
Note that even integers are treated as objects (of the int class). This is unlike C++ and Java (before version 1.5) where integers are primitive native types.
See help(int) for more details on the class.
Fields:
Variables that belong to an object or class are referred to as fields.
Instance variables and class variables:
Fields are of two types - they can belong to each instance/object of the class or they can belong to the class itself. They are called instance variables and class variables respectively.
Methods:
Objects can also have functionality by using functions that belong to a class. Such functions are called methods of the class.
Attributes:
the fields and methods can be referred to as the attributes of that class.
The self
Class methods have only one specific difference from ordinary functions - they must have an extra first name that has to be added to the beginning of the parameter list, but you do not give a value for this parameter when you call the method, Python will provide it. This particular variable refers to the object itself, and by convention, it is given the name self.
Say you have a class called MyClass and an instance of this class called myobject. When you call a method of this object as myobject.method(arg1, arg2), this is automatically converted by Python into MyClass.method(myobject, arg1, arg2) - this is all the special self is about.
This also means that if you have a method which takes no arguments, then you still have to have one argument - the self.
class Person:
def say_hi(self):
print('Hello, how are you?')
p = Person()
p.say_hi()
# The previous 2 lines can also be written as
# Person().say_hi()The __init__ method
There are many method names which have special significance in Python classes. We will see the significance of the __init__ method now.
The __init__ method is run as soon as an object of a class is instantiated. The method is useful to do any initialization you want to do with your object. Notice the double underscores both at the beginning and at the end of the name.
Example (save as oop_init.py):
class Person:
def __init__(self, name):
self.name = name
def say_hi(self):
print('Hello, my name is', self.name)
p = Person('Swaroop')
p.say_hi()
# The previous 2 lines can also be written as
# Person('Swaroop').say_hi()Output: $ python oop_init.py Hello, my name is Swaroop
Class And Object Variables
We have already discussed the functionality part of classes and objects (i.e. methods), now let us learn about the data part. The data part, i.e. fields, are nothing but ordinary variables that are bound to the namespaces of the classes and objects. This means that these names are valid within the context of these classes and objects only. That's why they are called name spaces.
There are two types of fields - class variables and object variables which are classified depending on whether the class or the object owns the variables respectively.
Public or private members:
All class members are public. One exception: If you use data members with names using the double underscore prefix such as __privatevar, Python uses name-mangling to effectively make it a private variable.
Thus, the convention followed is that any variable that is to be used only within the class or object should begin with an underscore and all other names are public and can be used by other classes/objects. Remember that this is only a convention and is not enforced by Python (except for the double underscore prefix).
Class variables are shared, they can be accessed by all instances of that class. There is only one copy of the class variable and when any one object makes a change to a class variable, that change will be seen by all the other instances.
En el siguiente ejemplo, population belongs to the Robot class and hence is a class variable. The name variable belongs to the object (it is assigned using self) and hence is an object variable.
Thus, we refer to the population class variable as Robot.population and not as self.population. We refer to the object variable name using self.name notation in the methods of that object.
Instead of Robot.population, we could have also used self.__class__.population because every object refers to its class via the self.__class__ attribute
Object variables are owned by each individual object/instance of the class. In this case, each object has its own copy of the field i.e. they are not shared and are not related in any way to the field by the same name in a different instance. An example will make this easy to understand (save as oop_objvar.py):
Class methods
The how_many method difined in the next example is actually a method that belongs to the class and not to the object. This means we can define it as either a classmethod or a staticmethod depending on whether we need to know which class we are part of. Since we refer to a class variable, let's use classmethod.
We have marked the how_many method as a class method using the classmethod decorator.
Decorators can be imagined to be a shortcut to calling a wrapper function, so applying the @classmethod decorator is same as calling:
how_many = classmethod(how_many)
Docstrings:
In this program, we also see the use of docstrings for classes as well as methods. We can access the class docstring at runtime using Robot.__doc__ and the method docstring as Robot.say_hi.__doc__
print(Robot.__doc__) print(Robot.say_hi.__doc__)
class Robot:
# Docstrings
"""Represents a robot, with a name."""
# A class variable, counting the number of robots
population = 0
def __init__(self, name):
# Docstrings
"""Initializes the data."""
self.name = name
print("(Initializing {})".format(self.name))
# When this person is created, the robot
# adds to the population
Robot.population += 1
def die(self):
"""I am dying."""
print("{} is being destroyed!".format(self.name))
Robot.population -= 1
if Robot.population == 0:
print("{} was the last one.".format(self.name))
else:
print("There are still {:d} robots working.".format(Robot.population))
def say_hi(self):
"""Greeting by the robot.
Yeah, they can do that."""
print("Greetings, my masters call me {}.".format(self.name))
@classmethod
def how_many(cls):
"""Prints the current population."""
print("We have {:d} robots.".format(cls.population))
droid1 = Robot("R2-D2")
droid1.say_hi()
Robot.how_many()
droid2 = Robot("C-3PO")
droid2.say_hi()
Robot.how_many()
print("\nRobots can do some work here.\n")
print("Robots have finished their work. So let's destroy them.")
droid1.die()
droid2.die()
Robot.how_many()
print(Robot.__doc__)
print(Robot.say_hi.__doc__)Output: $ python oop_objvar.py (Initializing R2-D2) Greetings, my masters call me R2-D2. We have 1 robots. (Initializing C-3PO) Greetings, my masters call me C-3PO. We have 2 robots. Robots can do some work here. Robots have finished their work. So let's destroy them. R2-D2 is being destroyed! There are still 1 robots working. C-3PO is being destroyed! C-3PO was the last one. We have 0 robots.
Inheritance
One of the major benefits of object oriented programming is reuse of code and one of the ways this is achieved is through the inheritance mechanism. Inheritance can be best imagined as implementing a type and subtype relationship between classes.
Suppose you want to write a program which has to keep track of the teachers and students in a college. They have some common characteristics such as name, age and address. They also have specific characteristics such as salary, courses and leaves for teachers and, marks and fees for students.
You can create two independent classes for each type and process them but adding a new common characteristic would mean adding to both of these independent classes. This quickly becomes unwieldy.
A better way would be to create a common class called SchoolMember and then have the teacher and student classes inherit from this class i.e. they will become sub-types of this type (class) and then we can add specific characteristics to these sub-types.
There are many advantages to this approach. If we add/change any functionality in SchoolMember, this is automatically reflected in the subtypes as well. For example, you can add a new ID card field for both teachers and students by simply adding it to the SchoolMember class. However, changes in the subtypes do not affect other subtypes. Another advantage is that if you can refer to a teacher or student object as a SchoolMember object which could be useful in some situations such as counting of the number of school members. This is called polymorphism where a sub-type can be substituted in any situation where a parent type is expected i.e. the object can be treated as an instance of the parent class.
Also observe that we reuse the code of the parent class and we do not need to repeat it in the different classes as we would have had to in case we had used independent classes.
The SchoolMember class in this situation is known as the base class or the superclass. The Teacher and Student classes are called the derived classes or subclasses.
We will now see this example as a program (save as oop_subclass.py):
class SchoolMember:
'''Represents any school member.'''
def __init__(self, name, age):
self.name = name
self.age = age
print('(Initialized SchoolMember: {})'.format(self.name))
def tell(self):
'''Tell my details.'''
print('Name:"{}" Age:"{}"'.format(self.name, self.age), end=" ")
class Teacher(SchoolMember):
'''Represents a teacher.'''
def __init__(self, name, age, salary):
SchoolMember.__init__(self, name, age)
self.salary = salary
print('(Initialized Teacher: {})'.format(self.name))
def tell(self):
SchoolMember.tell(self)
print('Salary: "{:d}"'.format(self.salary))
class Student(SchoolMember):
'''Represents a student.'''
def __init__(self, name, age, marks):
SchoolMember.__init__(self, name, age)
self.marks = marks
print('(Initialized Student: {})'.format(self.name))
def tell(self):
SchoolMember.tell(self)
print('Marks: "{:d}"'.format(self.marks))
t = Teacher('Mrs. Shrividya', 40, 30000)
s = Student('Swaroop', 25, 75)
# prints a blank line
print()
members = [t, s]
for member in members:
# Works for both Teachers and Students
member.tell()Output: $ python oop_subclass.py (Initialized SchoolMember: Mrs. Shrividya) (Initialized Teacher: Mrs. Shrividya) (Initialized SchoolMember: Swaroop) (Initialized Student: Swaroop) Name:"Mrs. Shrividya" Age:"40" Salary: "30000" Name:"Swaroop" Age:"25" Marks: "75"
Python Web Development
https://www.fullstackpython.com/web-development.html
Web Frameworks for Python:
https://wiki.python.org/moin/WebFrameworks
https://fr.wikipedia.org/wiki/Liste_de_frameworks_Python
Ejemplos de Web Applications in Python: https://wiki.python.org/moin/WebApplications
Complete Python Web Course (15€): https://www.udemy.com/the-complete-python-web-course-learn-by-building-8-apps/
Frameworks
Django
Página oficial: https://www.djangoproject.com/
Documentación: https://docs.djangoproject.com/en/1.11/
Instalación de django
Documentación oficial sobre la instalación: https://docs.djangoproject.com/en/1.11/topics/install/#installing-official-release
En este video se muestra la instalación dentro del virtual environement: https://www.youtube.com/watch?v=oRGK9klCn00#t=107.337193
Aquí se muestran distintas formas de instalar django: https://www.howtoforge.com/tutorial/how-to-install-django-on-ubuntu/
Prerequisitos
1. Install Python
Si ya está instalado. Setup python 3 as Default Python version: Ver Cambiar la versión por defecto
2. Install Apache and mod_wsgi
If you just want to experiment with Django, skip ahead to the next section; Django includes a lightweight web server you can use for testing, so you won’t need to set up Apache until you’re ready to deploy Django in production.
3. Get your database running
By default, the configuration uses SQLite. If you’re new to databases, or you’re just interested in trying Django, this is the easiest choice. SQLite is included in Python, so you won’t need to install anything else to support your database.
When starting your first real project, however, you may want to use a more scalable database like PostgreSQL, to avoid database-switching headaches down the road.
If you plan to use Django’s database API functionality, you’ll need to make sure a database server is running. Django supports many different database servers and is officially supported with PostgreSQL, MySQL, Oracle and SQLite.
4. Remove any old versions of Django
If you are upgrading your installation of Django from a previous version, you will need to uninstall the old Django version before installing the new version.
If you installed Django using pip or easy_install previously, installing with pip or easy_install again will automatically take care of the old version, so you don’t need to do it yourself.
If you previously installed Django using python setup.py install, uninstalling is as simple as deleting the django directory from your Python site-packages. To find the directory you need to remove, you can run the following at your shell prompt (not the interactive Python prompt):
$ python -c "import django; print(django.__path__)"
Install the Django code
1- Installing an official release with pip
1. Install pip.
https://www.howtoforge.com/tutorial/how-to-install-django-on-ubuntu/
The easiest is to use the standalone pip installer. If your distribution already has pip installed, you might need to update it if it’s outdated. If it’s outdated, you’ll know because installation won’t work.
Pip is a package management system for python. Python has a central package repository from which we can download the python package. It's called Python Package Index (PyPI).
In this tutorial, we will use python 3 for django as recommended by the django website. Next, we will install pip for python 3 from the ubuntu repository with this apt command:
apt-get install python3-pip
The installation will add a new binary file called 'pip3'. To make it easier to use pip, I will create a symlink for pip3 to pip:
Para saber donde se encuentra el ejecutable pip3: which pip3
Luego creamos el symlink: ln -s /usr/bin/pip3 /usr/bin/pip
Now check the version :
pip -V
Para actualizar pip a su más reciente versión:
pip install --upgrade pip
2. Installer virtualenv avec pip
Este programa permite crear un python virtual environment.
http://python-guide-pt-br.readthedocs.io/en/latest/dev/virtualenvs/
Take a look at virtualenv and virtualenvwrapper. These tools provide isolated Python environments, which are more practical than installing packages systemwide. They also allow installing packages without administrator privileges. The contributing tutorial walks through how to create a virtualenv on Python 3.
virtualenv is a tool to create isolated Python environments. Virtualenv creates a folder which contains all the necessary executables to use the packages that a Python project would need.
Install virtualenv via pip:
$ pip install virtualenv
Test your installation
$ virtualenv --version
Create a virtual environment for a project:
$ cd my_project_folder $ virtualenv my_project
O si estamos dentro del directorio donde queremos crear el virtual environment: $ virtualenv .
You can also use the Python interpreter of your choice (like python2.7).
$ virtualenv -p /usr/bin/python2.7 my_project
Then, to begin using the virtual environment, it needs to be activated:
$ source my_project/bin/activate
The name of the current virtual environment will now appear on the left of the prompt (e.g. (my_project)Your-Computer:your_project UserName$) to let you know that it’s active. From now on, any package that you install using pip will be placed in the my_project folder, isolated from the global Python installation.
Install packages as usual, for example:
pip install django==1.11
If you are done working in the virtual environment for the moment, you can deactivate it:
$ deactivate
This puts you back to the system’s default Python interpreter with all its installed libraries.
To delete a virtual environment, just delete its folder. (In this case, it would be rm -rf my_project.)
In order to keep your environment consistent, it’s a good idea to “freeze” the current state of the environment packages. To do this, run
$ pip freeze > requirements.txt
This will create a requirements.txt file, which contains a simple list of all the packages in the current environment, and their respective versions. You can see the list of installed packages without the requirements format using “pip list”. Later it will be easier for a different developer (or you, if you need to re-create the environment) to install the same packages using the same versions:
$ pip install -r requirements.txt
3. Install Django with Pip
After you’ve created and activated a virtual environment, enter the command:
pip install django==1.11 (ejecutarlo dentro del virtual environment)
Para ver la versión de django:
python import django print(django.get_version())
o a través de: django-admin --version
2- Installing a distribution-specific package
https://code.djangoproject.com/wiki/Distributions
Check the distribution specific notes to see if your platform/distribution provides official Django packages/installers. Distribution-provided packages will typically allow for automatic installation of dependencies and easy upgrade paths; however, these packages will rarely contain the latest release of Django.
En Ubuntu:
sudo apt-get install python3-django
Crear un proyecto django
https://docs.djangoproject.com/en/1.11/intro/tutorial01/
https://www.youtube.com/watch?v=oRGK9klCn00#t=107.337193
https://www.youtube.com/watch?v=kl3c00dq6BI&t=30s
Primero es apropiado crear un nuevo proyecto Sublime Text que contenga el directorio correspondiente a nuestro virtual environment. Ver Crear un proyecto en Sublime Text
Si hemos instalado django en un virtual envirnment, vamos al virtual environment (si no está activado el virtual environment, debemos hacerlo a través de "source bin/activate") (Ver Instalación de django) y ejecutamos el comando:
django-admin.py startproject nombre_proyecto
Si lo hemos instalado de forma global a través de un distribution-specific package, go into any directory where you’d like to store your code, then run the following command:
django-admin startproject nombre_proyecto
Vemos que la diferencia entre la instalación global y la instalación en el virtual environment es que en esta última el comando contiene la extensión .py.
Esto creará un directorio llamado "nombre_proyecto" dentro del cual se encuentra otro directorio con el mismo nombre. Se recomienda renombrar el directorio padre como, por ejemplo, "src".
mv nombre_proyecto src
Luego:
cd src python manage.py makemigrations python manage.py migrate
By running makemigrations, you’re telling Django that you’ve made some changes to your models (in this case, you’ve made new ones) and that you’d like the changes to be stored as a migration.
The migrate command looks at the INSTALLED_APPS setting and creates any necessary database tables according to the database settings in your mysite/settings.py file and the database migrations shipped with the app (we’ll cover those later).
Creación del superusuario:
Dentro del directorio src:
python manage.py createsuperuser
Luego podemos comprobar que nuestro proyecto django está corriendo correctamente ejecutando:
python manage.py runserver
Lo cual debe imprimir una línea como la siguiente:
Starting development server at http://127.0.0.1:8000/
Si copiamos http://127.0.0.1:8000/ en nuestro navegador internet, se abrirá una página que dice:
It worked! Congratulations on your first Django-powered page
Podemos también ingresar a:
http://127.0.0.1:8000/admin
Lo cual nos lleva hacia la página de administración de django. De hecho, esta aplicación corresponde con la orden:
INSTALLED_APPS = [ 'django.contrib.admin'
que se encuentra en settings.py. Si queremos ver el código fuente de django.contrib.admin podemos simplemente ir a google y colocar: django.contrib.admin code. Lo cual nos llevará a esta página: https://github.com/django/django/tree/master/django/contrib/admin en donde podemos ver todos los archivos que conforman la aplicación django.contrib.admin
Viendo estos códigos podemos entender y aprender a construir aplicaciones de este tipo.
Luego de detener la ejecución del comando runserver ya no podremos ingresar a: http://127.0.0.1:8000/
Como se mencionó, django.contrib.admin es un ejemplo de una aplicación django. Ahora, queremos crear nuestra propia aplicación. Para ello ejecutamos (en src):
python manage.py startapp nombre_app shortener
Vamos a crear una aplicación llamada shortener: python manage.py startapp shortener
https://www.youtube.com/watch?v=atNBuAjCDAs&t=306s
Models
Philosophy
A model is the single, definitive source of truth about your data. It contains the essential fields and behaviors of the data you’re storing. Django follows the DRY Principle. The goal is to define your data model in one place and automatically derive things from it.
En django se utiliza Models to map to the database. Es decir, debemos escribir un código en los Models to make a place to store our data.
Podríamos decir que un Modèle Django est un type particulier d'objet que nos permite interactuar con la base de datos.
Crear un Model
Para crear un model nos vamos al archivo models.py que ha sido creado dentro del directorio correspondiente a nuestra App (shorterner).
Este archivo luce así:
from django.db import models
# Create your models here.
class KirrURL(models.Model): # Creamos la clase KirrURL(cualquier nombre) that inherits(que proviene, que hereda) from models.Model
url = models.CharFied(max_length=220, ) # Creamos el campo
def __str__(self): # Definimos una función str
return str(self.url)
Luego tenemos que adicionar este modelo que hemos creado a admin.py. Para ello, nos vamos a dicho archivo:
from django.contrib import admin
# Register your models here. from .models import KirrURL
admin.site.register(KirrURL)
Luego tenemos que colocar neustra App (shortener) en INSTALLET_APPS del archivo settings.py.
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
# Mis Apps:
'shortener',
]
Luego debemos correr:
python manage.py makemigrations python manage.py migrate
Flask
Sublime Text
https://en.wikipedia.org/wiki/Sublime_Text
Sublime Text is a proprietary cross-platform source code editor with a Python application programming interface (API). It natively supports many programming languages and markup languages, and functions can be added by users with plugins.
Installation
https://linuxconfig.org/how-to-install-sublime-text-on-ubuntu-18-04-bionic-beaver-linux
wget -qO - https://download.sublimetext.com/sublimehq-pub.gpg | sudo apt-key add - sudo apt-add-repository "deb https://download.sublimetext.com/ apt/stable/"
sudo apt install sublime-text
Keyboard shortcut to comment lines in Sublime Text 3
http://stackoverflow.com/questions/17742781/keyboard-shortcut-to-comment-lines-in-sublime-text-3
https://forum.sublimetext.com/t/st3-3012-toggle-comments-broken/8930/8
As a workaround, go to Preferences->Key Bindings - User and add these keybindings (if you're using Linux):
{ "keys": ["ctrl+7"], "command": "toggle_comment", "args": { "block": false } },
{ "keys": ["ctrl+shift+7"], "command": "toggle_comment", "args": { "block": true } }
Indentation
https://stackoverflow.com/questions/9474090/how-do-i-force-sublime-text-to-indent-two-spaces-per-tab
If you want it for all files, go to Preferences -> Settings - Default/User. But as several comments below indicate, Syntax Specific settings can limit it to just the languages you choose.
To limit this configuration to Ruby files, first open up a Ruby file in the editor, and then go to Preferences -> Settings -> More -> Syntax Specific -> User. This should open a settings window named Ruby.sublime-settings
Save these settings:
{
"tab_size": 2,
"translate_tabs_to_spaces": true,
"detect_indentation": false
}
Crear un proyecto en Sublime Text
- Abrimos una nueva ventana: File > New Window
- Add folder to project
- Save project as: es apropiado guardarlo en el mismo directorio en donde fue creado el proyecto.
Esto creará dos archivos:
- nombre-111.sublime-project
- nombre-111.sublime-workspace : Este es el que debemos abrir para ingresar al proyecto.
