1654242910

# How to add two mixed numbers with unlike denominators

Learn how to add and subtract mixed numbers. Mixed numbers are numbers with two parts: the whole number part and the fraction part. Mixed numbers are ways to represent improper fractions using proper fractions.

To add or subtract mixed numbers, we first convert the mixed numbers to improper fractions by multiplying the whole number with the denominator and adding the result to the numerator, this becomes the new numerator and we retain the original denominator. After we have converted the mixed numbers to improper fraction we can now add or subtract the fractions.

Alternatively, we can split the mixed numbers and add the whole number parts separately and the fraction parts separately and then join the results.

1666082925

## 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
- 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.

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
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:

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

1645534030

## matrix multiplication in python user input

Given two user input matrix. Our task is to display the addition of two matrix. In these problem we use nested List comprehensive.

matrix multiplication in python user input

## Algorithm

Step1: input two matrix.

Step 2: nested for loops to iterate through each row and each column.

Step 3: take one resultant matrix which is initially contains all 0. Then we multiply each row elements of first matrix with each elements of second matrix, then add all multiplied value. That is the value of resultant matrix.

## Example Code

``````# Program to multiply two matrices
A=[]
n=int(input("Enter N for N x N matrix: "))
print("Enter the element ::>")
for i in range(n):
row=[]                                      #temporary list to store the row
for j in range(n):
row.append(int(input()))           #add the input to row list
A.append(row)                      #add the row to the list
print(A)
# [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
#Display the 2D array
print("Display Array In Matrix Form")
for i in range(n):
for j in range(n):
print(A[i][j], end=" ")
print()                                        #new line
B=[]
n=int(input("Enter N for N x N matrix : "))           #3 here
#use list for storing 2D array
#get the user input and store it in list (here IN : 1 to 9)
print("Enter the element ::>")
for i in range (n):
row=[]                                      #temporary list to store the row
for j in range(n):
row.append(int(input()))           #add the input to row list
B.append(row)                       #add the row to the list
print(B)
# [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
#Display the 2D array
print("Display Array In Matrix Form")
for i in range(n):
for j in range(n):
print(B[i][j], end=" ")
print()
result = [[0,0,0], [0,0,0], [0,0,0]]
for i in range(len(A)):
for j in range(len(B[0])):
for k in range(len(B)):
result[i][j] += A[i][k] * B[k][j]
print("The Resultant Matrix Is ::>")
for r in result:
print(r) ``````

## Output

``````Enter N for N x N matrix: 3
Enter the element ::>
2
1
4
2
1
2
3
4
3
[[2, 1, 4], [2, 1, 2], [3, 4, 3]]
Display Array In Matrix Form
2 1 4
2 1 2
3 4 3
Enter N for N x N matrix : 3
Enter the element ::>
1
2
3
4
5
6
7
8
9
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
Display Array In Matrix Form
1 2 3
4 5 6
7 8 9
The Resultant Matrix Is ::>
[34, 41, 48]
[20, 25, 30]
[40, 50, 60]``````

https://www.pakainfo.com/python-program-multiplication-of-two-matrix-from-user-input/

1641884883

## Como anexar A Uma Lista Ou Matriz Em Python Como Um Profissional

Neste artigo, você aprenderá sobre o `.append()`método em Python. Você também verá como `.append()`difere de outros métodos usados ​​para adicionar elementos a listas.

Vamos começar!

## O que são listas em Python? Uma definição para iniciantes

Uma matriz na programação é uma coleção ordenada de itens e todos os itens precisam ser do mesmo tipo de dados.

No entanto, ao contrário de outras linguagens de programação, os arrays não são uma estrutura de dados embutida no Python. Em vez de arrays tradicionais, o Python usa listas.

Listas são essencialmente arrays dinâmicos e são uma das estruturas de dados mais comuns e poderosas em Python.

Você pode pensar neles como contêineres ordenados. Eles armazenam e organizam tipos semelhantes de dados relacionados juntos.

Os elementos armazenados em uma lista podem ser de qualquer tipo de dados.

Pode haver listas de inteiros (números inteiros), listas de floats (números de ponto flutuante), listas de strings (texto) e listas de qualquer outro tipo de dados interno do Python.

Embora seja possível que as listas contenham apenas itens do mesmo tipo de dados, elas são mais flexíveis do que as matrizes tradicionais. Isso significa que pode haver uma variedade de tipos de dados diferentes dentro da mesma lista.

As listas têm 0 ou mais itens, o que significa que também pode haver listas vazias. Dentro de uma lista também pode haver valores duplicados.

Os valores são separados por uma vírgula e colocados entre colchetes, `[]`.

### Como criar listas em Python

Para criar uma nova lista, primeiro dê um nome à lista. Em seguida, adicione o operador de atribuição ( `=`) e um par de colchetes de abertura e fechamento. Dentro dos colchetes, adicione os valores que você deseja que a lista contenha.

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

#print the list to the console
print(names)

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

### Como as listas são indexadas em Python

As listas mantêm uma ordem para cada item.

Cada item na coleção tem seu próprio número de índice, que você pode usar para acessar o próprio item.

Índices em Python (e em qualquer outra linguagem de programação moderna) começam em 0 e aumentam para cada item da lista.

Por exemplo, a lista criada anteriormente tinha 4 valores:

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

O primeiro valor na lista, "Jimmy", tem um índice de 0.

O segundo valor na lista, "Timmy", tem um índice de 1.

O terceiro valor na lista, "Kenny", tem um índice de 2.

O quarto valor na lista, "Lenny", tem um índice de 3.

Para acessar um elemento na lista por seu número de índice, primeiro escreva o nome da lista, depois entre colchetes escreva o inteiro do índice do elemento.

Por exemplo, se você quisesse acessar o elemento que tem um índice de 2, você faria:

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

print(names[2])

#output
#Kenny
``````

## Listas em Python são mutáveis

Em Python, quando os objetos são mutáveis , significa que seus valores podem ser alterados depois de criados.

As listas são objetos mutáveis, portanto, você pode atualizá-las e alterá-las depois de criadas.

As listas também são dinâmicas, o que significa que podem crescer e diminuir ao longo da vida de um programa.

Os itens podem ser removidos de uma lista existente e novos itens podem ser adicionados a uma lista existente.

Existem métodos internos para adicionar e remover itens de listas.

Por exemplo, para add itens, há as `.append()`, `.insert()`e `.extend()`métodos.

Para remove itens, há as `.remove()`, `.pop()`e `.pop(index)`métodos.

### O que o `.append()`método faz?

O `.append()`método adiciona um elemento adicional ao final de uma lista já existente.

A sintaxe geral se parece com isso:

``````list_name.append(item)
``````

Vamos decompô-lo:

• `list_name` é o nome que você deu à lista.
• `.append()`é o método de lista para adicionar um item ao final de `list_name`.
• `item` é o item individual especificado que você deseja adicionar.

Ao usar `.append()`, a lista original é modificada. Nenhuma nova lista é criada.

Se você quiser adicionar um nome extra à lista criada anteriormente, faça o seguinte:

``````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']
``````

### Qual é a diferença entre os métodos `.append()`e `.insert()`?

A diferença entre os dois métodos é que `.append()`adiciona um item ao final de uma lista, enquanto `.insert()`insere um item em uma posição especificada na lista.

Como você viu na seção anterior, `.append()`irá adicionar o item que você passar como argumento para a função sempre no final da lista.

Se você não quiser apenas adicionar itens ao final de uma lista, poderá especificar a posição com a qual deseja adicioná-los `.insert()`.

A sintaxe geral fica assim:

``````list_name.insert(position,item)
``````

Vamos decompô-lo:

• `list_name` é o nome da lista.
• `.insert()` é o método de lista para inserir um item em uma lista.
• `position`é o primeiro argumento para o método. É sempre um número inteiro - especificamente é o número de índice da posição onde você deseja que o novo item seja colocado.
• `item`é o segundo argumento para o método. Aqui você especifica o novo item que deseja adicionar à lista.

Por exemplo, digamos que você tenha a seguinte lista de linguagens de programação:

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

print(programming_languages)

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

Se você quisesse inserir "Python" no início da lista, como um novo item da lista, você usaria o `.insert()`método e especificaria a posição como `0`. (Lembre-se de que o primeiro valor em uma lista sempre tem um índice de 0.)

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

programming_languages.insert(0, "Python")

print(programming_languages)

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

Se, em vez disso, você quisesse que "JavaScript" fosse o primeiro item da lista e, em seguida, adicionasse "Python" como o novo item, você especificaria a posição como `1`:

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

programming_languages.insert(1,"Python")

print(programming_languages)

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

O `.insert()`método oferece um pouco mais de flexibilidade em comparação com o `.append()`método que apenas adiciona um novo item ao final da lista.

### Qual é a diferença entre os métodos `.append()`e `.extend()`?

E se você quiser adicionar mais de um item a uma lista de uma só vez, em vez de adicioná-los um de cada vez?

Você pode usar o `.append()`método para adicionar mais de um item ao final de uma lista.

Digamos que você tenha uma lista que contém apenas duas linguagens de programação:

``````programming_languages = ["JavaScript", "Java"]

print(programming_languages)

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

Você então deseja adicionar mais dois idiomas, no final dele.

Nesse caso, você passa uma lista contendo os dois novos valores que deseja adicionar, como argumento para `.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++']]
``````

Se você observar mais de perto a saída acima, `['JavaScript', 'Java', ['Python', 'C++']]`, verá que uma nova lista foi adicionada ao final da lista já existente.

Então, `.append()` adiciona uma lista dentro de uma lista .

Listas são objetos, e quando você usa `.append()`para adicionar outra lista em uma lista, os novos itens serão adicionados como um único objeto (item).

Digamos que você já tenha duas listas, assim:

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

E se você quiser combinar o conteúdo de ambas as listas em uma, adicionando o conteúdo de `more_names`a `names`?

Quando o `.append()`método é usado para essa finalidade, outra lista é criada dentro 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']]
``````

Então, `.append()`adiciona os novos elementos como outra lista, anexando o objeto ao final.

Para realmente concatenar (adicionar) listas e combinar todos os itens de uma lista para outra , você precisa usar o `.extend()`método.

A sintaxe geral fica assim:

``````list_name.extend(iterable/other_list_name)
``````

Vamos decompô-lo:

• `list_name` é o nome de uma das listas.
• `.extend()` é o método para adicionar todo o conteúdo de uma lista a outra.
• `iterable`pode ser qualquer iterável, como outra lista, por exemplo, `another_list_name`. Nesse caso, `another_list_name`é uma lista que será concatenada com `list_name`, e seu conteúdo será adicionado um a um ao final de `list_name`, como itens separados.

Então, tomando o exemplo anterior, quando `.append()`for substituído por `.extend()`, a saída ficará assim:

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

names.extend(more_names)

print(names)

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

Quando usamos `.extend()`, a `names`lista foi estendida e seu comprimento aumentado em 2.

A maneira como `.extend()`funciona é que ele pega uma lista (ou outro iterável) como argumento, itera sobre cada elemento e, em seguida, cada elemento no iterável é adicionado à lista.

Há outra diferença entre `.append()`e `.extend()`.

Quando você deseja adicionar uma string, como visto anteriormente, `.append()`adiciona o item inteiro e único ao final da lista:

``````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']
``````

Se, em `.extend()`vez disso, você adicionasse uma string ao final de uma lista, cada caractere na string seria adicionado como um item individual à lista.

Isso ocorre porque as strings são iteráveis ​​e `.extend()`iteram sobre o argumento iterável passado para ela.

Então, o exemplo acima ficaria assim:

``````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']
``````

## Conclusão

Resumindo, o `.append()`método é usado para adicionar um item ao final de uma lista existente, sem criar uma nova lista.

Quando é usado para adicionar uma lista a outra lista, cria uma lista dentro de uma lista.

Se você quiser saber mais sobre Python, confira a Certificação Python do freeCodeCamp . Você começará a aprender de maneira interativa e amigável para iniciantes. Você também construirá cinco projetos no final para colocar em prática o que aprendeu.

#python

1593056092

## 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': '''
table code lists fullscreen  insertdatetime  nonbreaking
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 |
''',
'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

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.urls import path, include

urlpatterns = [
path('', include ('main.urls')),

]``````

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('', include ('main.urls')),
]

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):
...

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

1670560264

## 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.

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
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:

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