Array Indexing
Accessing individual elements and rows/columns by position
Knowledge Debt detected
You can study this freely — but your score may plateau if these foundations have gaps. The Mastery badge requires them to be solid.
Explanation
NumPy indexing works like Python lists for 1D, and extends naturally to multiple dimensions.
1D indexing:
python a = np.array([10, 20, 30, 40, 50]) a[0] # 10 (first) a[-1] # 50 (last) a[-2] # 40 (second to last)
2D indexing — [row, col]:
python m = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) m[0, 0] # 1 (top-left) m[1, 2] # 6 (row 1, col 2) m[-1, -1] # 9 (bottom-right) m[0] # [1, 2, 3] (entire first row) m[:, 1] # [2, 5, 8] (entire second column)
Examples
Row and column access
The colon : means "all" for that dimension
import numpy as np
m = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
print(m[1, 2]) # 6
print(m[2]) # [7 8 9] — row 2
print(m[:, 0]) # [1 4 7] — col 0
print(m[-1, -1]) # 9How well did you understand this?
Next in NumPy
Array Slicing