Indexing and Slicing Arrays

Indexing 1D arrays

1d

import numpy as np
 
arr = np.array([10, 20, 30, 40, 50])
 
print(arr[0])   # 10
print(arr[3])   # 40
print(arr[-1])  # 50

Slicing 1D arrays

Slicing uses start:stop:step (stop is excluded).

slice

import numpy as np
 
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]
print(arr[::-1])  # reverse

Indexing 2D arrays (rows/columns)

McKinney’s tip for reading 2D arrays: think of axis 0 as the “rows” and axis 1 as the “columns.”

2d

import numpy as np
 
mat = np.array([
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
])
 
print(mat[0, 0])  # 1
print(mat[1, 2])  # 6
print(mat[2, :])  # full row
print(mat[:, 1])  # full column

Sub-arrays (2D slicing)

subarray

import numpy as np
 
mat = np.array([
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
])
 
# rows 0..1 and cols 1..2
sub = mat[0:2, 1:3]
print(sub)

sketch Highlighting a 2D slice p5.js Pause Restart

mat[0:2, 1:3] selects rows 0-1 and columns 1-2 — the highlighted rectangle of the grid.

Views vs copies (important)

Many slices are views (they share memory).

view

import numpy as np
 
arr = np.array([1, 2, 3, 4, 5])
sub = arr[1:4]   # view
sub[0] = 999
 
print(arr)  # original changed

To force a copy:

copy

sub = arr[1:4].copy()

From the book

This trips up a lot of people coming from other languages: NumPy array slices are views, not copies. Modifying a slice modifies the original array. NumPy designed it this way on purpose — it would be far too slow and memory-hungry to copy data every time you slice a very large array. If you need an independent copy, call .copy() explicitly.

Boolean indexing (masking)

Boolean indexing is extremely useful for analytics filtering.

mask

import numpy as np
 
arr = np.array([10, 20, 30, 40, 50])
mask = arr > 25
 
print(mask)      # [False False  True  True  True]
print(arr[mask]) # [30 40 50]

Combine conditions

Use parentheses and & / | — the Python keywords and/or do not work with Boolean arrays.

mask2

import numpy as np
 
arr = np.array([10, 20, 30, 40, 50])
print(arr[(arr >= 20) & (arr <= 40)])

Fancy indexing

Select multiple specific indices using a list/array. Unlike slicing, fancy indexing always copies the data.

fancy

import numpy as np
 
arr = np.array([10, 20, 30, 40, 50])
print(arr[[0, 2, 4]])

diagram Four ways to select data mermaid

  flowchart LR
  A["ndarray"] --> B["Basic index
arr[2]"]
  A --> C["Slice
arr[1:4]"]
  A --> D["Boolean mask
arr[arr > 25]"]
  A --> E["Fancy index
arr[[0, 2, 4]]"]
  B --> F["Scalar or view"]
  C --> F
  D --> G["Copy"]
  E --> G

NumPy offers four selection styles, each returning a different shape and a view-or-copy behavior.

Next

Continue to: Shape Manipulation & Reshape to learn how to change dimensions safely.

🧪 Try It Yourself

Exercise 1 – Slice a 2D Sub-array

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Exercise 2 – Filter with a Boolean Mask

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Exercise 3 – A View Changes the Original

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