NumPy Indexing, Slicing, and Reshaping

Working with multi-dimensional data — tables, matrices, grids — is where NumPy truly separates itself from plain Python lists. Indexing, slicing, and reshaping arrays correctly is essential before moving into pandas, which is built directly on top of these same underlying concepts.

Indexing a 1D Array

arr = np.array([10, 20, 30, 40, 50])
print(arr[0])     # 10
print(arr[-1])    # 50 — last element

Indexing a 2D Array

matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

print(matrix[0])       # [1 2 3] — entire first row
print(matrix[1, 2])    # 6       — row index 1, column index 2
print(matrix[2][0])    # 7       — alternative syntax, same result

Slicing Arrays

arr = np.array([10, 20, 30, 40, 50])
print(arr[1:4])    # [20 30 40]
print(arr[:3])     # [10 20 30]
print(arr[::2])    # [10 30 50] — every second element

Slicing a 2D Array

matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

print(matrix[0:2, 1:3])
# [[2 3]
#  [5 6]]
# rows 0-1, columns 1-2

Views vs Copies — A Critical Distinction

arr = np.array([10, 20, 30, 40, 50])
sliced = arr[1:4]      # this is a VIEW, not an independent copy
sliced[0] = 999
print(arr)              # [ 10 999  30  40  50] — the ORIGINAL array changed too!

safe_copy = arr[1:4].copy()   # an explicit, independent copy
safe_copy[0] = 1
print(arr)                     # unaffected this time

This is one of the most common sources of subtle bugs for NumPy beginners: unlike slicing a plain Python list (which always copies), slicing a NumPy array returns a VIEW that shares the same underlying memory as the original. Use .copy() explicitly whenever you need an independent slice.

Boolean (Filtering) Indexing

scores = np.array([55, 90, 42, 78, 88])
passing = scores[scores >= 60]
print(passing)   # [90 78 88]

# What is actually happening under the hood:
print(scores >= 60)   # [False  True False  True  True] — a boolean mask

Reshaping an Array

arr = np.arange(1, 13)   # [1 2 3 ... 12], a flat 1D array of 12 elements

reshaped = arr.reshape(3, 4)
print(reshaped)
# [[ 1  2  3  4]
#  [ 5  6  7  8]
#  [ 9 10 11 12]]

The total number of elements must stay the same before and after reshaping — reshaping 12 elements into (3, 4) works because 3 × 4 = 12; attempting reshape(3, 5) would raise a ValueError.

Flattening a Multi-Dimensional Array

matrix = np.array([[1, 2, 3], [4, 5, 6]])
flat = matrix.flatten()
print(flat)   # [1 2 3 4 5 6]

Transposing a Matrix

matrix = np.array([[1, 2, 3], [4, 5, 6]])
print(matrix.T)
# [[1 4]
#  [2 5]
#  [3 6]]
# rows and columns are swapped

Interview tip: Be ready to explain why arr[1:4] on a NumPy array behaves differently from list_variable[1:4] on a Python list — the NumPy slice is a view sharing memory with the original array, while the list slice always creates a brand-new, independent list. This distinction is a frequent practical/code-reading interview question.