Using Python Array Module

Python
from array import array

# Create an array of signed integers ('i' typecode)
a = array('i', [1, 2, 3, 4, 5])
print(a)          # array('i', [1, 2, 3, 4, 5])
print(a[2])       # 3
print(a.tolist()) # [1, 2, 3, 4, 5]

Choose array over list when storing millions of numbers of the same type and memory footprint matters, or when you need to pass raw numeric buffers to C libraries, files, or sockets.

Progress:

• Step 1: Pick the correct typecode for the data's type and range
• Step 2: Construct the array with an iterable or bytes
• Step 3: Use list-like operations (append, extend, slicing, indexing)
• Step 4: Convert to/from bytes, lists, or files as needed
• Step 5: Validate itemsize and overflow behavior on the target platform

Step 1: Typecodes

Use array.itemsize to check actual byte size on the running platform — it is not guaranteed identical across platforms.

Step 2: Construction

Python
array(typecode)                 # empty array
array(typecode, iterable)       # from list/tuple/generator of numbers
array(typecode, bytes_obj)      # from raw bytes (interpreted per typecode)

Step 3: Common operations

Python
a.append(6)
a.extend([7, 8])
a.insert(0, -1)
a.pop()
a.remove(3)
a.reverse()
a[1:3] = array('i', [10, 20])   # slice assignment must use same typecode
len(a)
a.count(10)
a.index(10)

Step 4: Conversion

Python
a.tobytes()          # -> bytes
array('i').frombytes(b)   # populate from bytes
a.tolist()            # -> list
a.tofile(f)            # write to a binary file object
a.fromfile(f, n)       # read n items from a binary file object

Step 5: Buffer / memoryview interop

Python
mv = memoryview(a)
mv[0] = 99            # mutates the array in place