? Python Arrays

? Quick Overview

In Python, arrays are used to store multiple values of the same data type in a single variable. They come from the built-in array module and are more memory-efficient than lists when you are working with large amounts of numeric data.

? Key Concepts

Module based: Python arrays live in the array module, not built-in like lists.
Type code: Every array has a single data type, specified using a one-letter type code (for example, 'i' for signed integers).
Homogeneous data: All elements in an array must be of the same type.
Memory efficiency: Arrays use less memory than lists for large numeric datasets.
List-like operations: You can index, slice, append, insert, remove, and loop just like with lists.

? Syntax and Theory

To use arrays, you must first import the array module. Then you create an array by specifying a type code and an initial iterable of values:

Basic pattern:

array.array(typecode, iterable) # create an array with a type code and initial values

Common integer type codes:

'i' – signed integer
'I' – unsigned integer
'f' – floating point
'd' – double precision floating point

? Code Examples

? Creating an Integer Array

First, import the module and create an array of integers using the appropriate type code.

? View Code Example

import array

# Create an array of integers
numbers = array.array('i', [10, 20, 30, 40])
print(numbers)  # array('i', [10, 20, 30, 40])

? Accessing Array Elements

You can access elements by index, just like with lists. Indexing starts from 0.

? View Code Example

print(numbers[0])  # 10
print(numbers[2])  # 30

✏️ Modifying Array Elements

Change the value at a specific index by assigning a new value to that position.

? View Code Example

numbers[1] = 25  # change value at index 1
print(numbers)  # array('i', [10, 25, 30, 40])

➕ Adding and Removing Items

Arrays support common list-like methods such as append(), insert(), pop(), and remove().

? View Code Example

numbers.append(50)       # Add at end
numbers.insert(1, 15)    # Insert at index 1
numbers.pop()            # Remove last element
numbers.remove(30)       # Remove specific element
print(numbers)  # show final array contents

? Looping Through an Array

Use a for loop to visit each element in the array.

? View Code Example

# Loop through each value in the array
for num in numbers:
    print(num)

? Live Output and Explanation

? Sample Output Walkthrough

If you run all the examples in order, you will see something like this (values may vary based on your edits):

array('i', [10, 20, 30, 40])
10
30
array('i', [10, 25, 30, 40])
array('i', [10, 15, 25, 40, 50])
10
15
25
40
50

Notice how:

The first line shows the internal representation of the array object.
The next lines show values accessed by index.
After modification and insert/remove operations, the printed array reflects the new sequence of values.
The final block comes from looping through the array and printing each element.

✅ Tips

Use arrays when you need efficiency with numeric data and a fixed data type.
For mixed data types or general-purpose collections, prefer list over array.array.
For very large numeric datasets or scientific computing, consider NumPy arrays for even better performance and more features.
Always pick the correct type code so that your values fit without overflow or precision loss.

? Try It Yourself

Create an array of floats (type code 'f') and print all its elements.
Insert a new element at the beginning of an integer array using insert(0, value).
Ask the user for numbers, store them in an array, and then remove a specific number entered by the user.
Loop through an array and print only the even numbers.