Typed Sequences With Arrays

Although lists handle most jobs, Python also offers true arrays — fixed-type, memory-efficient sequences. The array module and the powerful numpy library expose typed arrays for situations where memory and speed matter, such as image processing, scientific computing, or game physics.

• Import the standard module: from array import array.
• Create with a type-code: array('i', [1, 2, 3]) for signed int.
• For numeric power, install and use NumPy: import numpy as np.
• Most array operations (sum, mean, sort) are vectorised — much faster than Python-level loops.

# Standard library
from array import array
nums = array('i', [10, 20, 30, 40])
nums.append(50)
print(nums[2])

# NumPy
import numpy as np
arr = np.array([1, 2, 3, 4])
print(arr * 2)            # [2 4 6 8]
print(arr.mean(), arr.sum())

• Why arrays vs lists?: Lists hold references to objects scattered in memory; arrays hold raw values contiguously. Arrays are smaller, faster to iterate, and friendly to C-level operations.
• Type codes: 'i' int, 'f' float, 'd' double, 'b' signed byte, 'u' unicode char. Only matching types may be inserted.
• Indexing and slicing: Identical to lists: arr[0], arr[-1], arr[1:4]. Slices return new arrays of the same type.
• Modifying arrays: Use append, extend, insert, pop, remove — the API matches lists.
• NumPy arrays: numpy.ndarray supports multi-dimensional shapes, broadcasting, and vectorised math. It is essential for data science, machine learning, and image processing.
• When to use what: Use list for general purpose; array.array for compact homogeneous numeric storage; numpy for math.

from array import array
nums = array("i", [10, 20, 30, 40])
nums.append(50)
for n in nums:
    print(n, end=" ")

from array import array
try:
    array("i", [1, 2, "three"])
except TypeError as e:
    print("TypeError:", e)

import numpy as np
x = np.array([1, 2, 3, 4, 5])
print(x * 2)
print(x + 10)
print(np.sqrt(x))

import numpy as np
scores = np.array([85, 92, 78, 90, 88])
print('Sum :', scores.sum())
print('Mean:', scores.mean())
print('Max :', scores.max())

import numpy as np
ages = np.array([15, 22, 30, 17, 45])
adults = ages[ages >= 18]
print(adults)

import numpy as np
a = np.arange(1, 13).reshape(3, 4)
print(a)

• Use the standard array only if all elements share a type and you don't need math operations.
• For any non-trivial numerical work, use NumPy — it's the foundation of the Python data ecosystem.
• NumPy arrays are not standard library — install with pip install numpy.
• Always prefer vectorised NumPy operations over Python for-loops for speed.
• Watch out for view vs copy semantics: arr[1:4] in NumPy is a view, not a copy.
• Combine NumPy with Matplotlib for instant data visualisation.

• Inserting wrong types: raises TypeError in the standard array.
• Treating NumPy slices as copies: modifying a slice changes the original.
• Using Python loops on NumPy arrays: defeats the speed benefit.
• Mixing list and array operations: some methods differ subtly between the two.
• Forgetting to import NumPy: np.array errors with NameError otherwise.
• Misusing append on NumPy: np.append returns a new array, not in-place.

• Problem 1: Create a NumPy array of 20 random integers and print mean, median, and standard deviation.
• Problem 2: Read a list of marks from the user and convert it to a NumPy array, then print the top 3 scores.
• Problem 3: Use the standard array module to store and process 1000 daily temperature readings.
• Problem 4: Reshape a 1-D NumPy array of 12 elements into a 3×4 matrix and print its transpose.
• Problem 5: Given two NumPy arrays of equal length, compute element-wise sum, difference, product.
• Problem 6: Filter all values greater than 50 from a NumPy array using boolean masking.

• Q1. What is the difference between a Python list and a Python array?
• Q2. Why is NumPy faster than a list for numeric work?
• Q3. Explain broadcasting in NumPy with an example.
• Q4. What is the difference between a view and a copy in NumPy?
• Q5. When would you use the standard array module over NumPy?
• Q6. What is vectorisation and why is it important?

• Q1: Why use an array instead of a list?
  Arrays use less memory and are faster when elements share one type. For numeric math, NumPy arrays are dramatically faster.
• Q2: Is NumPy part of the standard library?
  No — install it with pip install numpy. It is essentially the foundation of Python's data science stack.
• Q3: Can I store strings in a NumPy array?
  Yes, but it allocates fixed-width strings. For text-heavy work, use lists or pandas instead.
• Q4: What is broadcasting in NumPy?
  An automatic mechanism to align arrays of different shapes for element-wise operations — e.g. arr + 5.
• Q5: How do I convert a list to a NumPy array?
  Use np.array(my_list). To go back, use my_array.tolist().
• Q6: What is the difference between array.append and np.append?
  array.append mutates in place; np.append returns a new array (NumPy arrays are not resizable in place).