Array indexing
- Machine Learning course
- Beginner
- Lesson 10/112
- 5 min
In NumPy arrays, elements are accessed by index; the first element is at index 0.
Index and slice
In a two-dimensional array you write a[row, column]. Slices work as in Python lists; : means "all":
a = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
print(a[1, 2]) # 6
print(a[0]) # [1 2 3] first row
print(a[:, 1]) # [2 5 8] second column
print(a[:2, 1:]) # [[2 3] [5 6]]In ML, taking a single feature column with X[:, 0] is very common.
Boolean mask
A condition produces a True/False array of the same size. When that array is given as an index, only the elements meeting the condition are selected:
x = np.array([5, -2, 8, -1, 3])
print(x > 0) # [ True False True False True]
print(x[x > 0]) # [5 8 3]
x[x < 0] = 0 # zero the negatives
print(x) # [5 0 8 0 3]Several conditions are combined with & (and) and | (or); each condition must be in parentheses.
Practice Filter
Select the grades of 50 and above from the notlar array with a boolean mask and print them; then print how many there are.
Starter code
import numpy as np
notlar = np.array([45, 78, 50, 32, 91, 66])
# buraya yazSample solution
import numpy as np
notlar = np.array([45, 78, 50, 32, 91, 66])
gecen = notlar[notlar >= 50]
print(gecen)
print(len(gecen))Expected output
[78 50 91 66] 4
Questions and answers
Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.
1 For <code>a</code> above, what is <code>a[2, 0]</code>?
- 3
- 8
- 2
- 7 Correct answer
Explanation: Third row (index 2), first column (index 0): 7.
2 Take the first column from all rows of the matrix.
ilk = X[____]ilk = X[:, 0]Explanation: The first position selects rows (: = all), the second the column.
3 Match the expressions with what they select.
| Term | Meaning |
|---|---|
a[0] |
First row |
a[:, 0] |
First column |
a[-1] |
Last row |
a[a > 5] |
Those meeting the condition |