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Understanding Arrays in Python

Learn how to use Python arrays. Create arrays in Python using the array module. You'll see how to define them and the different methods commonly used for performing operations on them.
 

The artcile covers arrays that you create by importing the array module. We won't cover NumPy arrays here.

Table of Contents

1. Introduction to Arrays
   1. The differences between Lists and Arrays
   2. When to use arrays
2. How to use arrays
   1. Define arrays
   2. Find the length of arrays
   3. Array indexing
   4. Search through arrays
   5. Loop through arrays
   6. Slice an array
3. Array methods for performing operations
   1. Change an existing value
   2. Add a new value
   3. Remove a value
4. Conclusion

Let's get started!

---

What are Python Arrays?

Arrays are a fundamental data structure, and an important part of most programming languages. In Python, they are containers which are able to store more than one item at the same time.

Specifically, they are an ordered collection of elements with every value being of the same data type. That is the most important thing to remember about Python arrays - the fact that they can only hold a sequence of multiple items that are of the same type.

What's the Difference between Python Lists and Python Arrays?

Lists are one of the most common data structures in Python, and a core part of the language.

Lists and arrays behave similarly.

Just like arrays, lists are an ordered sequence of elements.

They are also mutable and not fixed in size, which means they can grow and shrink throughout the life of the program. Items can be added and removed, making them very flexible to work with.

However, lists and arrays are not the same thing.

Lists store items that are of various data types. This means that a list can contain integers, floating point numbers, strings, or any other Python data type, at the same time. That is not the case with arrays.

As mentioned in the section above, arrays store only items that are of the same single data type. There are arrays that contain only integers, or only floating point numbers, or only any other Python data type you want to use.

When to Use Python Arrays

Lists are built into the Python programming language, whereas arrays aren't. Arrays are not a built-in data structure, and therefore need to be imported via the array module in order to be used.

Arrays of the array module are a thin wrapper over C arrays, and are useful when you want to work with homogeneous data.

They are also more compact and take up less memory and space which makes them more size efficient compared to lists.

If you want to perform mathematical calculations, then you should use NumPy arrays by importing the NumPy package. Besides that, you should just use Python arrays when you really need to, as lists work in a similar way and are more flexible to work with.

How to Use Arrays in Python

In order to create Python arrays, you'll first have to import the array module which contains all the necassary functions.

There are three ways you can import the array module:

1. By using import array at the top of the file. This includes the module array. You would then go on to create an array using array.array().

import array

#how you would create an array
array.array()

1. Instead of having to type array.array() all the time, you could use import array as arr at the top of the file, instead of import array alone. You would then create an array by typing arr.array(). The arr acts as an alias name, with the array constructor then immediately following it.

import array as arr

#how you would create an array
arr.array()

1. Lastly, you could also use from array import *, with * importing all the functionalities available. You would then create an array by writing the array() constructor alone.

from array import *

#how you would create an array
array()

How to Define Arrays in Python

Once you've imported the array module, you can then go on to define a Python array.

The general syntax for creating an array looks like this:

variable_name = array(typecode,[elements])

Let's break it down:

• variable_name would be the name of the array.
• The typecode specifies what kind of elements would be stored in the array. Whether it would be an array of integers, an array of floats or an array of any other Python data type. Remember that all elements should be of the same data type.
• Inside square brackets you mention the elements that would be stored in the array, with each element being separated by a comma. You can also create an empty array by just writing variable_name = array(typecode) alone, without any elements.

Below is a typecode table, with the different typecodes that can be used with the different data types when defining Python arrays:

TYPECODE	C TYPE	PYTHON TYPE	SIZE
'b'	signed char	int	1
'B'	unsigned char	int	1
'u'	wchar_t	Unicode character	2
'h'	signed short	int	2
'H'	unsigned short	int	2
'i'	signed int	int	2
'I'	unsigned int	int	2
'l'	signed long	int	4
'L'	unsigned long	int	4
'q'	signed long long	int	8
'Q'	unsigned long long	int	8
'f'	float	float	4
'd'	double	float	8

Tying everything together, here is an example of how you would define an array in Python:

import array as arr 

numbers = arr.array('i',[10,20,30])


print(numbers)

#output

#array('i', [10, 20, 30])

Let's break it down:

• First we included the array module, in this case with import array as arr .
• Then, we created a numbers array.
• We used arr.array() because of import array as arr .
• Inside the array() constructor, we first included i, for signed integer. Signed integer means that the array can include positive and negative values. Unsigned integer, with H for example, would mean that no negative values are allowed.
• Lastly, we included the values to be stored in the array in square brackets.

Keep in mind that if you tried to include values that were not of i typecode, meaning they were not integer values, you would get an error:

import array as arr 

numbers = arr.array('i',[10.0,20,30])


print(numbers)

#output

#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", line 14, in <module>
#   numbers = arr.array('i',[10.0,20,30])
#TypeError: 'float' object cannot be interpreted as an integer

In the example above, I tried to include a floating point number in the array. I got an error because this is meant to be an integer array only.

Another way to create an array is the following:

from array import *

#an array of floating point values
numbers = array('d',[10.0,20.0,30.0])

print(numbers)

#output

#array('d', [10.0, 20.0, 30.0])

The example above imported the array module via from array import * and created an array numbers of float data type. This means that it holds only floating point numbers, which is specified with the 'd' typecode.

How to Find the Length of an Array in Python

To find out the exact number of elements contained in an array, use the built-in len() method.

It will return the integer number that is equal to the total number of elements in the array you specify.

import array as arr 

numbers = arr.array('i',[10,20,30])


print(len(numbers))

#output
# 3

In the example above, the array contained three elements – 10, 20, 30 – so the length of numbers is 3.

Array Indexing and How to Access Individual Items in an Array in Python

Each item in an array has a specific address. Individual items are accessed by referencing their index number.

Indexing in Python, and in all programming languages and computing in general, starts at 0. It is important to remember that counting starts at 0 and not at 1.

To access an element, you first write the name of the array followed by square brackets. Inside the square brackets you include the item's index number.

The general syntax would look something like this:

array_name[index_value_of_item]

Here is how you would access each individual element in an array:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[0]) # gets the 1st element
print(numbers[1]) # gets the 2nd element
print(numbers[2]) # gets the 3rd element

#output

#10
#20
#30

Remember that the index value of the last element of an array is always one less than the length of the array. Where n is the length of the array, n - 1 will be the index value of the last item.

Note that you can also access each individual element using negative indexing.

With negative indexing, the last element would have an index of -1, the second to last element would have an index of -2, and so on.

Here is how you would get each item in an array using that method:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[-1]) #gets last item
print(numbers[-2]) #gets second to last item
print(numbers[-3]) #gets first item
 
#output

#30
#20
#10

How to Search Through an Array in Python

You can find out an element's index number by using the index() method.

You pass the value of the element being searched as the argument to the method, and the element's index number is returned.

import array as arr 

numbers = arr.array('i',[10,20,30])

#search for the index of the value 10
print(numbers.index(10))

#output

#0

If there is more than one element with the same value, the index of the first instance of the value will be returned:

import array as arr 


numbers = arr.array('i',[10,20,30,10,20,30])

#search for the index of the value 10
#will return the index number of the first instance of the value 10
print(numbers.index(10))

#output

#0

How to Loop through an Array in Python

You've seen how to access each individual element in an array and print it out on its own.

You've also seen how to print the array, using the print() method. That method gives the following result:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers)

#output

#array('i', [10, 20, 30])

What if you want to print each value one by one?

This is where a loop comes in handy. You can loop through the array and print out each value, one-by-one, with each loop iteration.

For this you can use a simple for loop:

import array as arr 

numbers = arr.array('i',[10,20,30])

for number in numbers:
    print(number)
    
#output
#10
#20
#30

You could also use the range() function, and pass the len() method as its parameter. This would give the same result as above:

import array as arr  

values = arr.array('i',[10,20,30])

#prints each individual value in the array
for value in range(len(values)):
    print(values[value])

#output

#10
#20
#30

How to Slice an Array in Python

To access a specific range of values inside the array, use the slicing operator, which is a colon :.

When using the slicing operator and you only include one value, the counting starts from 0 by default. It gets the first item, and goes up to but not including the index number you specify.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#get the values 10 and 20 only
print(numbers[:2])  #first to second position

#output

#array('i', [10, 20])

When you pass two numbers as arguments, you specify a range of numbers. In this case, the counting starts at the position of the first number in the range, and up to but not including the second one:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])


#get the values 20 and 30 only
print(numbers[1:3]) #second to third position

#output

#rray('i', [20, 30])

Methods For Performing Operations on Arrays in Python

Arrays are mutable, which means they are changeable. You can change the value of the different items, add new ones, or remove any you don't want in your program anymore.

Let's see some of the most commonly used methods which are used for performing operations on arrays.

How to Change the Value of an Item in an Array

You can change the value of a specific element by speficying its position and assigning it a new value:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#change the first element
#change it from having a value of 10 to having a value of 40
numbers[0] = 40

print(numbers)

#output

#array('i', [40, 20, 30])

How to Add a New Value to an Array

To add one single value at the end of an array, use the append() method:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40)

print(numbers)

#output

#array('i', [10, 20, 30, 40])

Be aware that the new item you add needs to be the same data type as the rest of the items in the array.

Look what happens when I try to add a float to an array of integers:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40.0)

print(numbers)

#output

#Traceback (most recent call last):
#  File "/Users/dionysialemonaki/python_articles/demo.py", line 19, in <module>
#   numbers.append(40.0)
#TypeError: 'float' object cannot be interpreted as an integer

But what if you want to add more than one value to the end an array?

Use the extend() method, which takes an iterable (such as a list of items) as an argument. Again, make sure that the new items are all the same data type.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integers 40,50,60 to the end of numbers
#The numbers need to be enclosed in square brackets

numbers.extend([40,50,60])

print(numbers)

#output

#array('i', [10, 20, 30, 40, 50, 60])

And what if you don't want to add an item to the end of an array? Use the insert() method, to add an item at a specific position.

The insert() function takes two arguments: the index number of the position the new element will be inserted, and the value of the new element.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 in the first position
#remember indexing starts at 0

numbers.insert(0,40)

print(numbers)

#output

#array('i', [40, 10, 20, 30])

How to Remove a Value from an Array

To remove an element from an array, use the remove() method and include the value as an argument to the method.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30])

With remove(), only the first instance of the value you pass as an argument will be removed.

See what happens when there are more than one identical values:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30, 10, 20])

Only the first occurence of 10 is removed.

You can also use the pop() method, and specify the position of the element to be removed:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

#remove the first instance of 10
numbers.pop(0)

print(numbers)

#output

#array('i', [20, 30, 10, 20])

Conclusion

And there you have it - you now know the basics of how to create arrays in Python using the array module. Hopefully you found this guide helpful.

You'll start from the basics and learn in an interacitve and beginner-friendly way. You'll also build five projects at the end to put into practice and help reinforce what you learned.

Thanks for reading and happy coding!

Original article source at https://www.freecodecamp.org

#python 

Ajouter En Python - Comment Ajouter à Une Liste Ou à Un Tableau

Dans cet article, vous découvrirez la .append()méthode en Python. Vous verrez également en quoi .append()diffère des autres méthodes utilisées pour ajouter des éléments aux listes.

Commençons!

Que sont les listes en Python ? Une définition pour les débutants

Un tableau en programmation est une collection ordonnée d'éléments, et tous les éléments doivent être du même type de données.

Cependant, contrairement à d'autres langages de programmation, les tableaux ne sont pas une structure de données intégrée à Python. Au lieu des tableaux traditionnels, Python utilise des listes.

Les listes sont essentiellement des tableaux dynamiques et sont l'une des structures de données les plus courantes et les plus puissantes de Python.

Vous pouvez les considérer comme des conteneurs commandés. Ils stockent et organisent ensemble des données similaires.

Les éléments stockés dans une liste peuvent être de n'importe quel type de données.

Il peut y avoir des listes d'entiers (nombres entiers), des listes de flottants (nombres à virgule flottante), des listes de chaînes (texte) et des listes de tout autre type de données Python intégré.

Bien qu'il soit possible pour les listes de ne contenir que des éléments du même type de données, elles sont plus flexibles que les tableaux traditionnels. Cela signifie qu'il peut y avoir une variété de types de données différents dans la même liste.

Les listes ont 0 ou plusieurs éléments, ce qui signifie qu'il peut également y avoir des listes vides. À l'intérieur d'une liste, il peut également y avoir des valeurs en double.

Les valeurs sont séparées par une virgule et placées entre crochets, [].

Comment créer des listes en Python

Pour créer une nouvelle liste, donnez d'abord un nom à la liste. Ajoutez ensuite l'opérateur d'affectation ( =) et une paire de crochets ouvrants et fermants. A l'intérieur des parenthèses, ajoutez les valeurs que la liste doit contenir.

#create a new list of names
names = ["Jimmy", "Timmy", "Kenny", "Lenny"]

#print the list to the console
print(names)

#output
#['Jimmy', 'Timmy', 'Kenny', 'Lenny']

Comment les listes sont indexées en Python

Les listes maintiennent un ordre pour chaque article.

Chaque élément de la collection a son propre numéro d'index, que vous pouvez utiliser pour accéder à l'élément lui-même.

Les index en Python (et tout autre langage de programmation moderne) commencent à 0 et augmentent pour chaque élément de la liste.

Par exemple, la liste créée précédemment avait 4 valeurs :

names = ["Jimmy", "Timmy", "Kenny", "Lenny"]

La première valeur de la liste, "Jimmy", a un indice de 0.

La deuxième valeur de la liste, "Timmy", a un indice de 1.

La troisième valeur de la liste, "Kenny", a un indice de 2.

La quatrième valeur de la liste, "Lenny", a un indice de 3.

Pour accéder à un élément de la liste par son numéro d'index, écrivez d'abord le nom de la liste, puis entre crochets écrivez l'entier de l'index de l'élément.

Par exemple, si vous vouliez accéder à l'élément qui a un index de 2, vous feriez :

names = ["Jimmy", "Timmy", "Kenny", "Lenny"]

print(names[2])

#output
#Kenny

Les listes en Python sont modifiables

En Python, lorsque les objets sont mutables , cela signifie que leurs valeurs peuvent être modifiées une fois qu'ils ont été créés.

Les listes sont des objets modifiables, vous pouvez donc les mettre à jour et les modifier après leur création.

Les listes sont également dynamiques, ce qui signifie qu'elles peuvent augmenter et diminuer tout au long de la vie d'un programme.

Des éléments peuvent être supprimés d'une liste existante et de nouveaux éléments peuvent être ajoutés à une liste existante.

Il existe des méthodes intégrées pour ajouter et supprimer des éléments des listes.

Par exemple, pour add articles, il y a les .append(), .insert()et les .extend()méthodes.

Pour supprimer des éléments, il existe les méthodes .remove(), .pop()et .pop(index).

A quoi sert la .append()méthode ?

La .append()méthode ajoute un élément supplémentaire à la fin d'une liste déjà existante.

La syntaxe générale ressemble à ceci :

list_name.append(item)

Décomposons-le :

• list_name est le nom que vous avez donné à la liste.
• .append()est la méthode de liste pour ajouter un élément à la fin de list_name.
• item est l'élément individuel spécifié que vous souhaitez ajouter.

Lors de l'utilisation de .append(), la liste d'origine est modifiée. Aucune nouvelle liste n'est créée.

Si vous souhaitez ajouter un nom supplémentaire à la liste créée précédemment, procédez comme suit :

names = ["Jimmy", "Timmy", "Kenny", "Lenny"]

#add the name Dylan to the end of the list
names.append("Dylan")

print(names)

#output
#['Jimmy', 'Timmy', 'Kenny', 'Lenny', 'Dylan']

Quelle est la différence entre les méthodes .append()et .insert()?

La différence entre les deux méthodes est qu'elle .append()ajoute un élément à la fin d'une liste, alors qu'elle .insert()insère et élément à une position spécifiée dans la liste.

Comme vous l'avez vu dans la section précédente, .append()ajoutera l'élément que vous passez comme argument à la fonction toujours à la fin de la liste.

Si vous ne souhaitez pas simplement ajouter des éléments à la fin d'une liste, vous pouvez spécifier la position à laquelle vous souhaitez les ajouter avec .insert().

La syntaxe générale ressemble à ceci :

list_name.insert(position,item)

Décomposons-le :

• list_name est le nom de la liste.
• .insert() est la méthode de liste pour insérer un élément dans une liste.
• positionest le premier argument de la méthode. C'est toujours un entier - en particulier c'est le numéro d'index de la position où vous voulez que le nouvel élément soit placé.
• itemest le deuxième argument de la méthode. Ici, vous spécifiez le nouvel élément que vous souhaitez ajouter à la liste.

Par exemple, supposons que vous disposiez de la liste suivante de langages de programmation :

programming_languages = ["JavaScript", "Java", "C++"]

print(programming_languages)

#output
#['JavaScript', 'Java', 'C++']

Si vous vouliez insérer "Python" au début de la liste, en tant que nouvel élément de la liste, vous utiliseriez la .insert()méthode et spécifieriez la position comme 0. (Rappelez-vous que la première valeur d'une liste a toujours un indice de 0.)

programming_languages = ["JavaScript", "Java", "C++"]

programming_languages.insert(0, "Python")

print(programming_languages)

#output
#['Python', 'JavaScript', 'Java', 'C++']

Si vous aviez plutôt voulu que "JavaScript" soit le premier élément de la liste, puis ajoutez "Python" comme nouvel élément, vous spécifieriez la position comme suit1 :

programming_languages = ["JavaScript", "Java", "C++"]

programming_languages.insert(1,"Python")

print(programming_languages)

#output
#['JavaScript', 'Python', 'Java', 'C++']

La .insert()méthode vous donne un peu plus de flexibilité par rapport à la .append()méthode qui ajoute uniquement un nouvel élément à la fin de la liste.

Quelle est la différence entre les méthodes .append()et .extend()?

Que faire si vous souhaitez ajouter plusieurs éléments à une liste à la fois, au lieu de les ajouter un à la fois ?

Vous pouvez utiliser la .append()méthode pour ajouter plusieurs éléments à la fin d'une liste.

Supposons que vous ayez une liste qui ne contient que deux langages de programmation :

programming_languages = ["JavaScript", "Java"]

print(programming_languages)

#output
#['JavaScript', 'Java']

Vous souhaitez ensuite ajouter deux autres langues, à la fin.

Dans ce cas, vous passez une liste contenant les deux nouvelles valeurs que vous souhaitez ajouter, en argument à .append():

programming_languages = ["JavaScript", "Java"]

#add two new items to the end of the list
programming_languages.append(["Python","C++"])

print(programming_languages)

#output
#['JavaScript', 'Java', ['Python', 'C++']]

Si vous regardez de plus près la sortie ci-dessus, ['JavaScript', 'Java', ['Python', 'C++']]vous verrez qu'une nouvelle liste a été ajoutée à la fin de la liste déjà existante.

Donc, .append() ajoute une liste à l'intérieur d'une liste .

Les listes sont des objets, et lorsque vous utilisez .append()pour ajouter une autre liste dans une liste, les nouveaux éléments seront ajoutés en tant qu'objet unique (élément).

Supposons que vous ayez déjà deux listes, comme ceci :

names = ["Jimmy", "Timmy"]
more_names = ["Kenny", "Lenny"]

Et si vous vouliez combiner le contenu des deux listes en une seule, en ajoutant le contenu de more_namesto names?

Lorsque la .append()méthode est utilisée à cette fin, une autre liste est créée à l'intérieur de names:

names = ["Jimmy", "Timmy"]
more_names = ["Kenny", "Lenny"]

#add contents of more_names to names
names.append(more_names)

print(names)

#output
#['Jimmy', 'Timmy', ['Kenny', 'Lenny']]

Donc, .append()ajoute les nouveaux éléments comme une autre liste, en ajoutant l'objet à la fin.

Pour réellement concaténer (ajouter) des listes et combiner tous les éléments d'une liste à une autre , vous devez utiliser la .extend()méthode.

La syntaxe générale ressemble à ceci :

list_name.extend(iterable/other_list_name)

Décomposons-le :

• list_name est le nom de l'une des listes.
• .extend() est la méthode pour ajouter tout le contenu d'une liste à une autre.
• iterablepeut être n'importe quel itérable tel qu'une autre liste, par exemple, another_list_name. Dans ce cas, another_list_nameest une liste qui sera concaténée avec list_name, et son contenu sera ajouté un par un à la fin de list_name, en tant qu'éléments séparés.

Ainsi, en reprenant l'exemple précédent, lorsque .append()est remplacé par .extend(), la sortie ressemblera à ceci :

names = ["Jimmy", "Timmy"]
more_names = ["Kenny", "Lenny"]

names.extend(more_names)

print(names)

#output
#['Jimmy', 'Timmy', 'Kenny', 'Lenny']

Lorsque nous avons utilisé .extend(), la namesliste s'est allongée et sa longueur a été augmentée de 2.

La façon dont cela .extend()fonctionne est qu'il prend une liste (ou un autre itérable) comme argument, itère sur chaque élément, puis chaque élément de l'itérable est ajouté à la liste.

Il existe une autre différence entre .append()et .extend().

Lorsque vous souhaitez ajouter une chaîne, comme vu précédemment, .append()ajoutez l'élément entier et unique à la fin de la liste :

names = ["Jimmy", "Timmy", "Kenny", "Lenny"]

#add the name Dylan to the end of the list
names.append("Dylan")

print(names)

#output
#['Jimmy', 'Timmy', 'Kenny', 'Lenny', 'Dylan']

Si vous aviez .extend()plutôt l' habitude d'ajouter une chaîne à la fin d'une liste, chaque caractère de la chaîne serait ajouté en tant qu'élément individuel à la liste.

C'est parce que les chaînes sont un itérable et .extend()qu'elles itèrent sur l'argument itérable qui lui est transmis.

Ainsi, l'exemple ci-dessus ressemblerait à ceci :

names = ["Jimmy", "Timmy", "Kenny", "Lenny"]

#pass a string(iterable) to .extend()
names.extend("Dylan")

print(names)

#output
#['Jimmy', 'Timmy', 'Kenny', 'Lenny', 'D', 'y', 'l', 'a', 'n']

Conclusion

En résumé, la .append()méthode est utilisée pour ajouter un élément à la fin d'une liste existante, sans créer de nouvelle liste.

Lorsqu'il est utilisé pour ajouter une liste à une autre liste, il crée une liste dans une liste.

Si vous souhaitez en savoir plus sur Python, consultez la certification Python de freeCodeCamp . Vous commencerez à apprendre de manière interactive et conviviale pour les débutants. Vous construirez également cinq projets à la fin pour mettre en pratique ce que vous avez appris.

Merci d'avoir lu et bon codage!

Link: https://www.freecodecamp.org/news/append-in-python-how-to-append-to-a-list-or-an-array/

#python 

Top 6 Python Packages You Should be Using in Every Django Web App

There are countless Python packages easily added to any project. But there are some packages you can't help but use in every Django web app because they've proven to be extremely beneficial and time-saving.

We decided to focus on those packages, the ones you'll end up installing regularly, and explain the installation and configurations needed to get them up and running. 

While some Python packages offer cool functionality needed for one specific project, the packages discussed below are the bread-and-butter of the Django packages.

Django Web Framework

But we can't jump into Django packages by talking about the Django web framework.

A web framework is comprised of modules or packages that allow developers to quickly write web applications without having to handle the precise details of the protocol and other web app management.

Django is considered a full-stack web framework in which a database, application server, template engine, authentication module, and dispatcher are all neatly combined to create a high-level framework. These individual components are included upon package installation and often just need some minor configurations for them to function correctly. 

macOS Terminal

(env)User-Macbook:env user$ pip install django

Windows Command Prompt

(env)C:\Users\Owner\desktop\env> pip install django

At the time of this article, the latest version of Django is 3.0.8. To install the latest version, all you need is the command pip install django.

If you wish to install a different version, then specify the version number as demonstrated in the command pip install django==2.1.15. Please note that there are two equal signs after the package name, not one. 

Once the installation is complete, you will need to start configuring your Django web app with a project and an application. If you want to jump right into building your Django web app, check out the quick start guides to Django Installation and Django Configuration. Or if you are just getting started and need a step-by-step tutorial, see the Beginner's Guide to Django Web Apps. 

But we are here to talk about Python Packages meant for Django web apps, not basic Django configurations so we'll keep moving.

We have a lot to cover.

1. Django TinyMCE4 Lite
2. Pillow
3. Django Crispy Forms
4. Django Tables
5. Django Filter
6. Python Decouple

 

---

 

(1) Django TinyMCE4 Lite

macOS Terminal

(env)User-Macbook:mysite user$ pip install django-tinymce4-lite

Windows Command Prompt

(env) C:\Users\Owner\Desktop\Code\env\mysite>pip install django-tinymce4-lite

Once you have finished the basic configurations of your web app, you can install a cool Python package named django-tinymce4-lite. This package is actually a smaller version of the Django application django-tinymce4 that contains a widget to render Django form fields as TinyMCE editors.

TinyMCE is a WYSIWYG ("what you see is what you get") text editor that converts HTML elements into editor instances or "plain text".  This python package is highly recommended if you are looking to create a blog as you can easily edit text that is then formatted to HTML within the actual template.

 

env > mysite > mysite > settings.py

INSTALLED_APPS = [
    ...
    ...
    'tinymce',
]


TINYMCE_DEFAULT_CONFIG = {
    'height': 400,
    'width': 1000,
    'cleanup_on_startup': True,
    'custom_undo_redo_levels': 20,
    'selector': 'textarea',
    'browser_spellcheck': 'True',
    'theme': 'modern',
    'plugins': '''
            textcolor save link image media preview codesample contextmenu
            table code lists fullscreen  insertdatetime  nonbreaking
            contextmenu directionality searchreplace wordcount visualblocks
            visualchars code fullscreen autolink lists  charmap print  hr
            anchor pagebreak
            ''',
    'toolbar1': '''
            fullscreen preview bold italic underline | fontselect,
            fontsizeselect  | forecolor backcolor | alignleft alignright |
            aligncenter alignjustify | indent outdent | bullist numlist table |
            | link image media | codesample
            ''',
    'toolbar2': '''
            visualblocks visualchars |
            charmap hr pagebreak nonbreaking anchor |  code |
            ''',
    'contextmenu': 'formats | link image',
    'menubar': True,
    'statusbar': True,
    }

After installation, you will need to add tinymce to the list of installed apps in the settings file then add the default configurations below.  The default configurations define the height, weight, spellcheck, and toolbars. 

 

env > mysite > mysite > urls.py

"""mysite URL Configuration

The `urlpatterns` list routes URLs to views. For more information please see:
    https://docs.djangoproject.com/en/2.1/topics/http/urls/
Examples:
Function views
    1. Add an import:  from my_app import views
    2. Add a URL to urlpatterns:  path('', views.home, name='home')
Class-based views
    1. Add an import:  from other_app.views import Home
    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')
Including another URLconf
    1. Import the include() function: from django.urls import include, path
    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))
"""
from django.contrib import admin
from django.urls import path, include


urlpatterns = [
    path('admin/', admin.site.urls),
    path('', include ('main.urls')),
    path('tinymce/', include('tinymce.urls')), #add this

]

Then add the TinyMCE path to the project URLs.

 

env > mysite > main > models.py

from django.db import models
from tinymce import HTMLField

class MyModel(models.Model):
    ...
    content = HTMLField()

Finally, you can quickly add TinyMCE to the Django model by importing HTMLField at the top of the page then calling it in the model field. If you are unsure of how to use Django models, check out the article, How to use Django Models for more information. 

 

---

 

(2) Pillow

macOS Terminal

(env)User-Macbook:mysite user$ pip install Pillow

Windows Command Prompt

(env) C:\Users\Owner\Desktop\Code\env\mysite>pip install Pillow

So, this package is not specific to Django but is needed for image and file uploads to work correctly in a Django project.  If you are looking to have a media upload field in your Django model for let's say an article cover image, you need to install Pillow. It's a Python Imaging Library fork for uploading files correctly. 

 

env > mysite > mysite > settings.py

MEDIA_URL = '/media/'

MEDIA_ROOT = os.path.join(BASE_DIR, 'media')

Once installed, you need to add a media folder URL and ROOT directory to your settings file. 

 

env > mysite > mysite > urls.py

from django.contrib import admin
from django.urls import path, include
from django.conf import settings #add this
from django.conf.urls.static import static #add this

urlpatterns = [
    path('admin/', admin.site.urls),
    path('', include ('main.urls')),
]

if settings.DEBUG: #add this
    urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

Then you need to add the necessary imports at the top of your project's URL file and specify the URL pattern to the media folder. Keep in mind that the media upload will not work in production given the if condition. You will need to reconfigure your media upload location when you are ready to deploy.

 

env > mysite > main > models.py

from django.db import models

class MyModel(models.Model):
    ...
    image = models.ImageField(upload_to='images/')

Now to upload an image, go to your models file and add an ImageField with the upload location as 'images/'. The uploaded images will then be added to a media  > images folder that will automatically be created upon the upload. 

For more information about correctly creating a model, accessing the upload location in the Django admin, and rendering the model in a template, refer to How to use Django Models.

 

---

 

(3) Django Crispy Forms

macOS Terminal

(env)User-Macbook:mysite user$ pip install django-crispy-forms

Windows Command Prompt

(env) C:\Users\Owner\desktop\code\env\mysite>pip install django-crispy-forms

Let's talk about Django forms. Their functionality is great but their appearance isn't the best. You can choose to install django-crispy-forms in your project to quickly solve this issue.

 

env > mysite > mysite > settings.py

INSTALLED_APPS = [
    ...
    'crispy_forms',
]

CRISPY_TEMPLATE_PACK = 'uni_form'

For it to function correctly, you will need to go to the settings file and add crispy_forms to the installed apps list. Keep in mind that there is an underscore between crispy and forms.

Then you need to specify the crispy template pack. The one listed below is the default but if you are using the Bootstrap CSS framework, check out how to integrate Bootstrap with django-crispy-forms. 

 

env > mysite > main > templates > main > contact.html

{% load crispy_forms_tags %}

<form method="post">
    {% csrf_token %}
       {{form|crispy}}
       <button type="submit">Submit</button>
</form>

The package django-crispy-forms is added to the project in the form of a filter added within the Django template language {{form}}. This format will not only call all of the form fields but also format each field according to the crispy form template pack specified in the settings.

Refer to the article Render Forms with Django Crispy Forms for more information regarding the form rendering process using crispy forms and the article Build a Django Contact Form with Email Backend for more general information on how to build a Django form. 

 

---

 

(4) Django Tables

macOS Terminal

(env)User-Macbook:mysite user$ pip install django-tables2

Windows Command Prompt

(env) C:\Users\Owner\desktop\code\env\mysite>pip install django-tables2

Now let's say you want to create a dynamic table in your Django project that connects to a model. Install django-tables2, a Django-specific package for table rendering.

 

env > mysite > mysite > settings.py

INSTALLED_APPS = [
    ...
    'django_tables2',
]

Add Django tables to the installed apps.

 

env > mysite > main > models.py

from django.db import models


class MyModel(models.Model):
    name = models.CharField(max_length=100, verbose_name="full name")
    email = models.EmailField(max_length=200)

Then create the model you wish to use in the table.

After you have created the model, you will need to run the commands python manage.py makemigrations and python manage.py migrate to add the model to the database and add your model objects via the Django admin. For more instruction, see How to Use Django Models. 

 

env > mysite > main > (New File) tables.py

import django_tables2 as tables
from .models import MyModel

class MyTable(tables.Table):
    class Meta:
        model = MyModel
        fields = ("name", "email", )

 

Now, create a new file called tables.py in the application folder, main, and import tables from django_tables2 at the top of the file. Then create a class that specifies the model and field names. 

 

env > mysite > main > views.py (Class-based views)

...
from django_tables2 import SingleTableView

from .models import MyModel
from .tables import MyTable


class ListView(SingleTableView):
    model = MyModel
    table_class = MyTable
    template_name = 'main/table.html'

 

If you are looking to use class-based views, go to the views file and add the view class specifying the model, table, and template. Again, you will need to import the necessary variables from their appropriate files at the top of the file.

 

env > mysite > main > urls.py (Class-based views)

from django.urls import path
from . import views

app_name = "main"   


urlpatterns = [
     path("table", views.ListView.as_view()),
]

Then make sure there is a tables URL in the app urls.py file. If you are looking to learn more about class-based views, check out the article Django Class-based Views.

 

env > mysite > main > views.py (Function-based views)

...
from django_tables2 import SingleTableView

from .models import MyModel
from .tables import MyTable


def list(request):
	model = MyModel.objects.all()
	table = MyTable(model)
	return render(request=request, template_name="main/table.html", context={"model":model, "table":table})

 

Or you can choose to do function-based views in the views.py file. Either one will work, but the format is different. 

 

env > mysite > main > urls.py (Function-based views)

from django.urls import path
from . import views

app_name = "main"   


urlpatterns = [
    path("table", views.list, name="list"),
]

Then add the table URL in the app urls.py file. 

 

env > mysite > main > templates > main > (New File) table.html

{% load render_table from django_tables2 %}

<div>
    {% render_table table %}
</div>

With the views and URLs configured, you can render the table in the template by loading in render_table from django_tables2 at the top of the file then calling render_table and the context of the table passed in the view.

By default, the class-based view passes the table context as just table, and in the function-based view, we also chose to specify the context of the table as table. 

 

If you want to add Bootstrap CSS to the table:

env > mysite > main > tables.py

import django_tables2 as tables
from .models import MyModel

class MyTable(tables.Table):
    class Meta:
        model = MyModel
        template_name = "django_tables2/bootstrap4.html"
        fields = ("name", "email",)

 

Add a template name to the tables.py file connecting to the Bootstrap template. This and other template files can be found in the Lib > site-packages > django_tables2 > templates > django_tables2 folder of your project.

 

env > mysite > main > templates > main > (New File) table.html

{% extends "main/header.html" %}

{% block content %}

{% load render_table from django_tables2 %}

<div class="container">
    {% render_table table %}
</div>

{% endblock %}

Then you can extend to a header that loads in the Bootstrap CDNs. This is the easiest way of adding Bootstrap to all of your templates using the same piece of code.

If you are unsure of how to use the extends tag with the Bootstrap CDNs, check out the Django extends tag and block content section in the Beginner's Guide to Django Web Apps. 

 

---

 

(5) Django Filter

macOS Terminal

(env)User-Macbook:mysite user$  pip install django-filter

Windows Command Prompt

(env) C:\Users\Owner\desktop\code\env\mysite>  pip install django-filter

Now that you have a table, you probably want the ability to search for specific content within the rows and filter the table by its results. The django-filter package can easily be used on top of the django-tables2 package to accomplish this.

 

env > mysite > mysite > settings.py

INSTALLED_APPS = [
    ...
    'django_filters',
]

Add Django filters to the installed apps. Note that is django_filters not django_filter.

 

env > mysite > main > (New File) filters.py

import django_filters
from .models import MyModel


class MyFilter(django_filters.FilterSet):
	name = django_filters.CharFilter(lookup_expr='icontains')

	class Meta:
		model = MyModel
		fields = {'name', 'email'}

 

Now, create a new file called filters.py in the application folder, main, and import django_filters. Then list the model and the model fields you wish to filter by.

You can also choose to add django_filters.CharFilter to the class. In the example above, the filter displays any rows where the name column contains the query specified. 

You can also choose to do django_filters.CharFilter(lookup_expr='iexact') if you are looking to filter only by an exact query match.

 

env > mysite > main > views.py (Class-based views)

...
from django_tables2 import SingleTableMixin
from django_filters.views import FilterView

from .models import MyModel
from .tables import MyTable
from .filters import MyFilter


class ListView(SingleTableMixin, FilterView):
    model = MyModel
    table_class = MyTable
    template_name = 'main/table.html'
    filterset_class = MyFilter

 

Then for a class-based view, import FilterView from django_filters.views at the top of the file and change django_tables2 import from SingleTableView to SingleTableMixin. You will also need to import your custom filter from the filter.py file.

In the class view, ListView will now inherit SingleTableMixin and FilterView and list the filterset_class as the custom filter within it. 

 

env > mysite > main > templates > main > table.html

{% load render_table from django_tables2 %}

<div>
    <br>
    <form action="" method="GET">
        {{filter.form}}
        <button type="submit">Filter</button>
    </form>
    <br>
    {% render_table table %}
</div>

With class-based views, the URL will stay the same but you will need to add a form HTML element and the Django Template language calling the filter and the form within the template. You also need a submit button within the form to submit your filter queries. Nothing changes about the way the table renders.

 

env > mysite > main > views.py (Function-based views)

...
from django_tables2.views import SingleTableMixin
from django_filter import FilterView

from .models import MyModel
from .tables import MyTable


def list(request):
	model = MyModel.objects.all()
	filterset_class = MyFilter(request.GET, model)
	table = MyTable(filterset_class.qs)
	return render(request=request, template_name="main/table.html", context={"model":model, "table":table, "filterset_class":filterset_class})

 

If using function-based views, make the same imports and the class-based views, then create an instance of the MyFilter class and pass in a GET request and model as arguments. Pass in the filterset_class as a queryset argument in the table then lists the filterset_class as context in the return render. 

 

env > mysite > main > templates > main > table.html

{% load render_table from django_tables2 %}

<div>
    <br>
    <form action="" method="GET">
        {{filterset_class.form}}
        <button type="submit">Filter</button>
    </form>
    <br>
    {% render_table table %}
</div>

With function-based views, you will need to specify the filterset_class, or the context declared, as the filter on the form. Everything else is the same format as the class-based template.

If you are looking to style the form, either scroll back up to the Django Crispy Forms section or click at the article mentioned earlier, Render Forms with Django Crispy Forms.

 

---

 

(6) Python Decouple

macOS Terminal

(env)User-Macbook:mysite user$ pip install python-decouple

Windows Command Prompt

(env) C:\Users\Owner\desktop\code\env\mysite> pip install python-decouple

The last and arguably most important Python package we will discuss is python-decouple. This package hides your sensitive configuration keys and information from hackers. It was created for Django but it is now considered a "generic tool" for separating configuration settings.

 

env > mysite > (New File) .env

SECRET_KEY =sdjioerb43buobnodhioh4i34hgip
DEBUG =True

env > mysite > mysite > settings.py

from decouple import config

SECRET_KEY = config('SECRET_KEY')
DEBUG = config('DEBUG', cast=bool)

Create a new file named .env in the project folder then import config in the settings.py file. Then transfer all of the configuration settings and variables you wish to hide to the .env file and call each variable using the python-decouple format of config('variable').

#programming #django #python

A Procedural Macro for Defining Nom Combinators in Simple DSL For Rust

nom-rule  

A procedural macro for defining nom combinators in simple DSL. Requires nom v5.0+.

Dependencies

[dependencies]
nom = "7"
nom-rule = "0.2"

Syntax

The procedural macro rule! provided by this crate is designed for the ease of writing grammar spec as well as to improve maintainability, it follows these simple rules:

1. TOKEN: match the token by token kind. You should provide a parser to eat the next token if the token kind matched. it will get expanded into match_token(TOKEN).
2. ";": match the token by token text. You should provide a parser to eat the next token if the token text matched. it will get expanded into match_text(";") in this example.
3. #fn_name: an external nom parser function. In the example above, ident is a predefined parser for identifiers.
4. a ~ b ~ c: a sequence of parsers to take one by one. It'll get expanded into nom::sequence::tuple.
5. (...)+: one or more repeated patterns. It'll get expanded into nom::multi::many1.
6. (...)*: zero or more repeated patterns. It'll get expanded into nom::multi::many0.
7. (...)?: Optional parser. It'll get expanded into nom::combinator::opt.
8. a | b | c: Choices between a, b, and c. It'll get expanded into nom::branch::alt.
9. &a: Peek. It'll get expanded into nom::combinator::peek(a). Note that it doesn't consume the input.
10. !a: Negative predicate. It'll get expanded into nom::combinator::not. Note that it doesn't consume the input.
11. ^a: Cut parser. It'll get expanded into nom::combinator::cut.
12. ... : "description": Context description for error reporting. It'll get expanded into nom::error::context.

Example

Define match_text parser and match_token parser for your custom token type. You can use nom::combinator::fail as match_token if your parser use &str or &[u8] as input because you won't match on token kinds.

#[derive(Clone, Debug, PartialEq)]
struct Token<'a> {
    kind: TokenKind,
    text: &'a str,
    span: Span,
}

#[derive(Clone, Copy, Debug, PartialEq)]
enum TokenKind {
    Whitespace,

    // Keywords
    CREATE,
    TABLE,

    // Symbols
    LParen,
    RParen,
    Semicolon,
    Comma,

    Ident,
}

fn match_text<'a, Error: ParseError<Input<'a>>>(
    text: &'a str,
) -> impl FnMut(Input<'a>) -> IResult<Input<'a>, &'a Token<'a>, Error> {
    move |i| satisfy(|token: &Token<'a>| token.text == text)(i)
}

fn match_token<'a, Error: ParseError<Input<'a>>>(
    kind: TokenKind,
) -> impl FnMut(Input<'a>) -> IResult<Input<'a>, &'a Token<'a>, Error> {
    move |i| satisfy(|token: &Token<'a>| token.kind == kind)(i)
}

Then give the two parser to nom_rule::rule! by wrapping it into a custom macro:

macro_rules! rule {
    ($($tt:tt)*) => { 
        nom_rule::rule!($crate::match_text, $crate::match_token, $($tt)*)
    }
}

To define a parser for the SQL of creating table:

let mut rule = rule!(
    CREATE ~ TABLE ~ #ident ~ ^"(" ~ (#ident ~ #ident ~ ","?)* ~ ")" ~ ";" : "CREATE TABLE statement"
);

It will get expanded into:

let mut rule = 
    nom::error::context(
        "CREATE TABLE statement",
        nom::sequence::tuple((
            (crate::match_token)(CREATE),
            (crate::match_token)(TABLE),
            ident,
            (nom::combinator::cut(crate::match_text)("(")),
            nom::multi::many0(nom::sequence::tuple((
                ident,
                ident,
                nom::combinator::opt((crate::match_text)(",")),
            ))),
            (crate::match_text)(")"),
            (crate::match_text)(";"),
        ))
    );

Auto Sequence (nightly only)

nom-rule is able to automatically insert ~ in the rule when necessary so that you get the example above working the same as the following:

let mut rule = rule!(
    CREATE TABLE #ident "(" (#ident #ident ","?)* ")" ";" : "CREATE TABLE statement"
);

To enable this feature, you need to use a nightly channel rust complier, and add this to the Cargo.toml:

nom-rule = { version = "0.2", features = ["auto-sequence"] }

---

Download Details:

Author: andylokandy
Source Code: https://github.com/andylokandy/nom-rule

License: MIT license

#rust 

How to Create Arrays in Python

In this tutorial, you'll know the basics of how to create arrays in Python using the array module. Learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.

This tutorialvideo on 'Arrays in Python' will help you establish a strong hold on all the fundamentals in python programming language. Below are the topics covered in this video:  
1:15 What is an array?
2:53 Is python list same as an array?
3:48  How to create arrays in python?
7:19 Accessing array elements
9:59 Basic array operations
        - 10:33  Finding the length of an array
        - 11:44  Adding Elements
        - 15:06  Removing elements
        - 18:32  Array concatenation
       - 20:59  Slicing
       - 23:26  Looping  

---

Python Array Tutorial – Define, Index, Methods

In this article, you'll learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.

The artcile covers arrays that you create by importing the array module. We won't cover NumPy arrays here.

Table of Contents

1. Introduction to Arrays
   1. The differences between Lists and Arrays
   2. When to use arrays
2. How to use arrays
   1. Define arrays
   2. Find the length of arrays
   3. Array indexing
   4. Search through arrays
   5. Loop through arrays
   6. Slice an array
3. Array methods for performing operations
   1. Change an existing value
   2. Add a new value
   3. Remove a value
4. Conclusion

Let's get started!

What are Python Arrays?

Arrays are a fundamental data structure, and an important part of most programming languages. In Python, they are containers which are able to store more than one item at the same time.

Specifically, they are an ordered collection of elements with every value being of the same data type. That is the most important thing to remember about Python arrays - the fact that they can only hold a sequence of multiple items that are of the same type.

What's the Difference between Python Lists and Python Arrays?

Lists are one of the most common data structures in Python, and a core part of the language.

Lists and arrays behave similarly.

Just like arrays, lists are an ordered sequence of elements.

They are also mutable and not fixed in size, which means they can grow and shrink throughout the life of the program. Items can be added and removed, making them very flexible to work with.

However, lists and arrays are not the same thing.

Lists store items that are of various data types. This means that a list can contain integers, floating point numbers, strings, or any other Python data type, at the same time. That is not the case with arrays.

As mentioned in the section above, arrays store only items that are of the same single data type. There are arrays that contain only integers, or only floating point numbers, or only any other Python data type you want to use.

When to Use Python Arrays

Lists are built into the Python programming language, whereas arrays aren't. Arrays are not a built-in data structure, and therefore need to be imported via the array module in order to be used.

Arrays of the array module are a thin wrapper over C arrays, and are useful when you want to work with homogeneous data.

They are also more compact and take up less memory and space which makes them more size efficient compared to lists.

If you want to perform mathematical calculations, then you should use NumPy arrays by importing the NumPy package. Besides that, you should just use Python arrays when you really need to, as lists work in a similar way and are more flexible to work with.

How to Use Arrays in Python

In order to create Python arrays, you'll first have to import the array module which contains all the necassary functions.

There are three ways you can import the array module:

• By using import array at the top of the file. This includes the module array. You would then go on to create an array using array.array().

import array

#how you would create an array
array.array()

• Instead of having to type array.array() all the time, you could use import array as arr at the top of the file, instead of import array alone. You would then create an array by typing arr.array(). The arr acts as an alias name, with the array constructor then immediately following it.

import array as arr

#how you would create an array
arr.array()

• Lastly, you could also use from array import *, with * importing all the functionalities available. You would then create an array by writing the array() constructor alone.

from array import *

#how you would create an array
array()

How to Define Arrays in Python

Once you've imported the array module, you can then go on to define a Python array.

The general syntax for creating an array looks like this:

variable_name = array(typecode,[elements])

Let's break it down:

• variable_name would be the name of the array.
• The typecode specifies what kind of elements would be stored in the array. Whether it would be an array of integers, an array of floats or an array of any other Python data type. Remember that all elements should be of the same data type.
• Inside square brackets you mention the elements that would be stored in the array, with each element being separated by a comma. You can also create an empty array by just writing variable_name = array(typecode) alone, without any elements.

Below is a typecode table, with the different typecodes that can be used with the different data types when defining Python arrays:

TYPECODE	C TYPE	PYTHON TYPE	SIZE
'b'	signed char	int	1
'B'	unsigned char	int	1
'u'	wchar_t	Unicode character	2
'h'	signed short	int	2
'H'	unsigned short	int	2
'i'	signed int	int	2
'I'	unsigned int	int	2
'l'	signed long	int	4
'L'	unsigned long	int	4
'q'	signed long long	int	8
'Q'	unsigned long long	int	8
'f'	float	float	4
'd'	double	float	8

Tying everything together, here is an example of how you would define an array in Python:

import array as arr 

numbers = arr.array('i',[10,20,30])


print(numbers)

#output

#array('i', [10, 20, 30])

Let's break it down:

• First we included the array module, in this case with import array as arr .
• Then, we created a numbers array.
• We used arr.array() because of import array as arr .
• Inside the array() constructor, we first included i, for signed integer. Signed integer means that the array can include positive and negative values. Unsigned integer, with H for example, would mean that no negative values are allowed.
• Lastly, we included the values to be stored in the array in square brackets.

Keep in mind that if you tried to include values that were not of i typecode, meaning they were not integer values, you would get an error:

import array as arr 

numbers = arr.array('i',[10.0,20,30])


print(numbers)

#output

#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", line 14, in <module>
#   numbers = arr.array('i',[10.0,20,30])
#TypeError: 'float' object cannot be interpreted as an integer

In the example above, I tried to include a floating point number in the array. I got an error because this is meant to be an integer array only.

Another way to create an array is the following:

from array import *

#an array of floating point values
numbers = array('d',[10.0,20.0,30.0])

print(numbers)

#output

#array('d', [10.0, 20.0, 30.0])

The example above imported the array module via from array import * and created an array numbers of float data type. This means that it holds only floating point numbers, which is specified with the 'd' typecode.

How to Find the Length of an Array in Python

To find out the exact number of elements contained in an array, use the built-in len() method.

It will return the integer number that is equal to the total number of elements in the array you specify.

import array as arr 

numbers = arr.array('i',[10,20,30])


print(len(numbers))

#output
# 3

In the example above, the array contained three elements – 10, 20, 30 – so the length of numbers is 3.

Array Indexing and How to Access Individual Items in an Array in Python

Each item in an array has a specific address. Individual items are accessed by referencing their index number.

Indexing in Python, and in all programming languages and computing in general, starts at 0. It is important to remember that counting starts at 0 and not at 1.

To access an element, you first write the name of the array followed by square brackets. Inside the square brackets you include the item's index number.

The general syntax would look something like this:

array_name[index_value_of_item]

Here is how you would access each individual element in an array:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[0]) # gets the 1st element
print(numbers[1]) # gets the 2nd element
print(numbers[2]) # gets the 3rd element

#output

#10
#20
#30

Remember that the index value of the last element of an array is always one less than the length of the array. Where n is the length of the array, n - 1 will be the index value of the last item.

Note that you can also access each individual element using negative indexing.

With negative indexing, the last element would have an index of -1, the second to last element would have an index of -2, and so on.

Here is how you would get each item in an array using that method:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers[-1]) #gets last item
print(numbers[-2]) #gets second to last item
print(numbers[-3]) #gets first item
 
#output

#30
#20
#10

How to Search Through an Array in Python

You can find out an element's index number by using the index() method.

You pass the value of the element being searched as the argument to the method, and the element's index number is returned.

import array as arr 

numbers = arr.array('i',[10,20,30])

#search for the index of the value 10
print(numbers.index(10))

#output

#0

If there is more than one element with the same value, the index of the first instance of the value will be returned:

import array as arr 


numbers = arr.array('i',[10,20,30,10,20,30])

#search for the index of the value 10
#will return the index number of the first instance of the value 10
print(numbers.index(10))

#output

#0

How to Loop through an Array in Python

You've seen how to access each individual element in an array and print it out on its own.

You've also seen how to print the array, using the print() method. That method gives the following result:

import array as arr 

numbers = arr.array('i',[10,20,30])

print(numbers)

#output

#array('i', [10, 20, 30])

What if you want to print each value one by one?

This is where a loop comes in handy. You can loop through the array and print out each value, one-by-one, with each loop iteration.

For this you can use a simple for loop:

import array as arr 

numbers = arr.array('i',[10,20,30])

for number in numbers:
    print(number)
    
#output
#10
#20
#30

You could also use the range() function, and pass the len() method as its parameter. This would give the same result as above:

import array as arr  

values = arr.array('i',[10,20,30])

#prints each individual value in the array
for value in range(len(values)):
    print(values[value])

#output

#10
#20
#30

How to Slice an Array in Python

To access a specific range of values inside the array, use the slicing operator, which is a colon :.

When using the slicing operator and you only include one value, the counting starts from 0 by default. It gets the first item, and goes up to but not including the index number you specify.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#get the values 10 and 20 only
print(numbers[:2])  #first to second position

#output

#array('i', [10, 20])

When you pass two numbers as arguments, you specify a range of numbers. In this case, the counting starts at the position of the first number in the range, and up to but not including the second one:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])


#get the values 20 and 30 only
print(numbers[1:3]) #second to third position

#output

#rray('i', [20, 30])

Methods For Performing Operations on Arrays in Python

Arrays are mutable, which means they are changeable. You can change the value of the different items, add new ones, or remove any you don't want in your program anymore.

Let's see some of the most commonly used methods which are used for performing operations on arrays.

How to Change the Value of an Item in an Array

You can change the value of a specific element by speficying its position and assigning it a new value:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#change the first element
#change it from having a value of 10 to having a value of 40
numbers[0] = 40

print(numbers)

#output

#array('i', [40, 20, 30])

How to Add a New Value to an Array

To add one single value at the end of an array, use the append() method:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40)

print(numbers)

#output

#array('i', [10, 20, 30, 40])

Be aware that the new item you add needs to be the same data type as the rest of the items in the array.

Look what happens when I try to add a float to an array of integers:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40.0)

print(numbers)

#output

#Traceback (most recent call last):
#  File "/Users/dionysialemonaki/python_articles/demo.py", line 19, in <module>
#   numbers.append(40.0)
#TypeError: 'float' object cannot be interpreted as an integer

But what if you want to add more than one value to the end an array?

Use the extend() method, which takes an iterable (such as a list of items) as an argument. Again, make sure that the new items are all the same data type.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integers 40,50,60 to the end of numbers
#The numbers need to be enclosed in square brackets

numbers.extend([40,50,60])

print(numbers)

#output

#array('i', [10, 20, 30, 40, 50, 60])

And what if you don't want to add an item to the end of an array? Use the insert() method, to add an item at a specific position.

The insert() function takes two arguments: the index number of the position the new element will be inserted, and the value of the new element.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 in the first position
#remember indexing starts at 0

numbers.insert(0,40)

print(numbers)

#output

#array('i', [40, 10, 20, 30])

How to Remove a Value from an Array

To remove an element from an array, use the remove() method and include the value as an argument to the method.

import array as arr 

#original array
numbers = arr.array('i',[10,20,30])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30])

With remove(), only the first instance of the value you pass as an argument will be removed.

See what happens when there are more than one identical values:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30, 10, 20])

Only the first occurence of 10 is removed.

You can also use the pop() method, and specify the position of the element to be removed:

import array as arr 

#original array
numbers = arr.array('i',[10,20,30,10,20])

#remove the first instance of 10
numbers.pop(0)

print(numbers)

#output

#array('i', [20, 30, 10, 20])

Conclusion

And there you have it - you now know the basics of how to create arrays in Python using the array module. Hopefully you found this guide helpful.

Thanks for reading and happy coding!

#python #programming