Indexing and Slicing

Slice Range

A sub-range of a 6-element list copied by an index loop, then extracted with slice syntax. The trace shows i stepping from start to stop - 1 and sliced growing one element per iteration.

By hand

Walk indices from start to stop - 1 with range(start, stop) and append each values[i] to sliced. This makes the copy explicit: each element is read individually and written into the new list.

naive.py
Replay: real traced execution (multi-file project)
values = [10, 20, 30, 40, 50, 60]
start = 1
stop = 4
sliced = []
for i in range(start, stop):
    sliced.append(values[i])
print('RESULT:', sliced)

  1. values ← [10, 20, 30, 40, 50, 60]

    1values = [10, 20, 30, 40, 50, 60]2start = 1
    values this step[10, 20, 30, 40, 50, 60]values

  2. start ← 1

    1values = [10, 20, 30, 40, 50, 60]2start = 13stop = 4
    values this step1start

  3. stop ← 4

    2start = 13stop = 44sliced = []
    values this step4stop

  4. sliced ← []

    3stop = 44sliced = []5for i in range(start, stop):
    values this step[]sliced

  5. i ← 1

    4sliced = []5for i in range(start, stop):6    sliced.append(values[i])
    values this step1i

  6. sliced ← [20]

    5for i in range(start, stop):6    sliced.append(values[i])7print('RESULT:', sliced)
    values this step[] → [20]sliced

  7. i ← 2

    4sliced = []5for i in range(start, stop):6    sliced.append(values[i])
    values this step1 → 2i

  8. sliced ← [20, 30]

    5for i in range(start, stop):6    sliced.append(values[i])7print('RESULT:', sliced)
    values this step[20] → [20, 30]sliced

  9. i ← 3

    4sliced = []5for i in range(start, stop):6    sliced.append(values[i])
    values this step2 → 3i

  10. sliced ← [20, 30, 40]

    5for i in range(start, stop):6    sliced.append(values[i])7print('RESULT:', sliced)
    values this step[20, 30] → [20, 30, 40]sliced

  11. for i in range(start, stop):

    4sliced = []5for i in range(start, stop):6    sliced.append(values[i])

  12. stdout ← RESULT: [20, 30, 40]

    6    sliced.append(values[i])7print('RESULT:', sliced)
    values this stepRESULT: [20, 30, 40]stdout

With NumPy

a[start:stop] returns a view of elements at indices start through stop - 1 without an explicit loop. The snapshot shows the sliced array's shape, dtype, and values.

library.py 
import numpy as np

values = [10, 20, 30, 40, 50, 60]
start = 1
stop = 4
a = np.array(values)
sliced = a[start:stop]
print('shape:', sliced.shape)
print('dtype:', sliced.dtype)
print('values:', sliced.tolist())
print('RESULT:', sliced.tolist())

shape: (3,)
dtype: int64
values: [20, 30, 40]
RESULT: [20, 30, 40]

Implementation notes

  • A NumPy slice returns a view into the original buffer — no copy is made. Modifying sliced[0] also modifies a[start]. Call .copy() on the slice to get an independent array.
  • Python list slicing (values[1:4]) always produces a copy. The "By hand" loop makes this copy explicit element-by-element.
  • Shape, dtype, and values are shown explicitly here because ndarray.__repr__ output varies with NumPy version and print options.