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Python arrays provide several built-in methods to inspect, modify, and manipulate array data efficiently

Common Array Functions

`buffer_info()`

The buffer_info() method returns important low-level details about the array.

What it returns:

A tuple containing:

Memory address of the array
Number of elements stored in the array

Example:

from array import *

vals = array('i', [5, 9, 8, 4, 2])
print(vals.buffer_info())

Output (example):

#(address, length)
()

Use Case:

Helpful when working with memory management, low-level programming, or interfacing with C-based libraries.

`typecode`

The typecode attribute returns the data type of elements stored in the array.

Example:

print(vals.typecode)

Output:

Use Case:

Useful when the array comes from an external source (e.g., file or network) and the data type is not known in advance.

`append()`

Adds a new element to the end of the array.

Example:

vals.append(10)
print(vals)

Output:

array('i', [5, 9, 8, 4, 2, 10])

The appended value must match the array’s typecode.

`remove()`

Removes the first occurrence of a specified value.

Example:

vals.remove(8)
print(vals)

Output:

array('i', [5, 9, 4, 2])

`reverse()`

Reverses the order of elements in-place.

Example:

vals.reverse()
print(vals)

Output:

array('i', [2, 4, 9, 5])

reverse() modifies the original array, not a copy.

Example: Using Multiple Array Functions Together

from array import *

vals = array('i', [5, 9, 8, 4, 2])

print("Buffer Info:", vals.buffer_info())
print("Type Code:", vals.typecode)

vals.append(10)
vals.remove(8)
vals.reverse()

print("Final Array:", vals)

Copying and Transforming Arrays

Arrays can be copied or transformed using generator expressions for better memory efficiency.

1. Copying an Existing Array (Using Generator Expression)

from array import *

vals = array('i', [5, 9, 8, 4, 2])
newArr = array(vals.typecode, (a for a in vals))

for e in newArr:
    print(e)

Why This Is Efficient:

Avoids creating an intermediate list
Consumes less memory than tolist()

2. Creating a Derived Array (Square of Elements)

vals = array('i', [5, 9, 8, 4, 2])
newArr = array(vals.typecode, (a * a for a in vals))

for e in newArr:
    print(e)

Output:

Converting Array to List

lst = a.tolist()

Note:

tolist() creates an extra list in memory
Generator-based copying is more memory-efficient

Arrays_Transformations

Reference vs Copy in Arrays

Assignment Creates a Reference (Not a Copy)

a = array('i', [1, 2, 3])
b = a

Changes in b will affect a
Both variables point to the same array

Creating an Actual Copy

b = array(a.typecode, (x for x in a))

Now a and b are independent arrays

Summary

Arrays are optimized for efficient storage and processing of single-type numeric data.
Generator expressions help reduce memory usage by avoiding unnecessary list creation.
Understanding typecode and reference vs copy behavior is essential to prevent logical errors.
Arrays are best suited for performance-critical and memory-efficient applications involving homogeneous data.

Written By: Muskan Garg

Array Functions

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