Python Academy · Lesson

Indexing, Slicing, and Fancy Indexing

Access and modify array elements with advanced indexing techniques.

Indexing, Slicing, and Fancy Indexing is a free Python Academy lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Python Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Basic Indexing

Access elements with zero-based integer indices. Negative indices count from the end. Multi-dimensional arrays use comma-separated indices.

import numpy as np

arr = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(arr[0, 2])    # 3
print(arr[-1, -1])  # 9
print(arr[1])       # [4 5 6]

Slicing

Slicing syntax start:stop:step returns a view. Applies independently on each dimension.

import numpy as np

m = np.arange(16).reshape(4,4)
print(m[1:3, 1:3])
# [[5 6]
#  [9 10]]
print(m[::2, ::2])   # every other row and column

Boolean Indexing (Masking)

Pass a boolean array of the same shape to select elements where True.

import numpy as np

arr = np.array([1, -2, 3, -4, 5])
mask = arr > 0
print(mask)         # [ True False  True False  True]
print(arr[mask])    # [1 3 5]
arr[~mask] = 0      # set negatives to 0
print(arr)          # [1 0 3 0 5]

Fancy Indexing with Integer Arrays

Pass an array of integer indices to select multiple specific elements.

import numpy as np

arr = np.array([10, 20, 30, 40, 50])
idx = np.array([0, 2, 4])
print(arr[idx])   # [10 30 50]

# 2-D fancy indexing:
m = np.arange(9).reshape(3,3)
print(m[[0,2], [0,2]])   # [m[0,0], m[2,2]] = [0, 8]

np.where for Indexed Selection

np.where(condition) (one argument) returns indices where condition is True.

import numpy as np

arr = np.array([5, 2, 8, 1, 9])
idx = np.where(arr > 4)[0]
print(idx)         # [0 2 4]
print(arr[idx])    # [5 8 9]

np.take and np.put

np.take(arr, indices) selects elements (supports flat indexing). np.put(arr, indices, values) inserts values.

import numpy as np

arr = np.array([10, 20, 30, 40])
print(np.take(arr, [1, 3]))   # [20 40]

np.put(arr, [0, 2], [99, 88])
print(arr)   # [99 20 88 40]

np.nonzero

np.nonzero(arr) returns a tuple of arrays — one per dimension — of the indices of non-zero elements.

import numpy as np

arr = np.array([[0,1,0],[2,0,3]])
rows, cols = np.nonzero(arr)
print(rows, cols)   # [0 1 1] [1 0 2]

Ellipsis Indexing

The ... (Ellipsis) expands to as many : as needed to cover remaining dimensions.

import numpy as np

arr = np.ones((2,3,4,5))
print(arr[0, ..., 2].shape)   # (3, 4) — first dim=0, last dim=2
print(arr[..., 0].shape)      # (2, 3, 4) — last dim=0

np.ix_ for Outer Indexing

np.ix_ constructs an open mesh from multiple 1-D sequences for outer-product-style fancy indexing.

import numpy as np

m = np.arange(25).reshape(5,5)
rows = [0, 2, 4]
cols = [1, 3]
print(m[np.ix_(rows, cols)])
# [[ 1  3]
#  [11 13]
#  [21 23]]

Assigning to Fancy Index

You can assign to a fancy-indexed selection in-place.

import numpy as np

arr = np.zeros(5)
arr[[1, 3]] = [10, 20]
print(arr)   # [ 0. 10.  0. 20.  0.]

# Broadcast a scalar:
arr[[0, 2, 4]] = 99
print(arr)   # [99. 10. 99. 20. 99.]

Difference: View vs Copy in Indexing

Basic/slice indexing → view (modifying changes original). Fancy/boolean indexing → copy (modifying does NOT change original).

import numpy as np

arr = np.arange(5)
view = arr[1:4]   # view
view[0] = 99
print(arr)         # [0 99 2 3 4]

copy = arr[[1,2,3]]   # fancy → copy
copy[0] = 0
print(arr)         # unchanged

Quick Check

Does boolean/fancy indexing return a view or a copy?

Recap

Use [row, col] for single elements, start:stop:step for slices (views), boolean arrays for masking (copies), and integer arrays for fancy indexing (copies). Use np.where to get indices, np.ix_ for outer meshes.

Frequently asked questions

Is the “Indexing, Slicing, and Fancy Indexing” lesson free?

Yes — the full text of “Indexing, Slicing, and Fancy Indexing” is free to read here on the web, and the Python Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Python Academy course, upgrade to CoddyKit PRO.

What will I learn in “Indexing, Slicing, and Fancy Indexing”?

Access and modify array elements with advanced indexing techniques. You practise Python Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Python Academy?

No prior experience is required. Python Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Indexing, Slicing, and Fancy Indexing” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this Python Academy lesson?

Yes. Every Python Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

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