Summary - NumPy basics
- Machine Learning course
- Beginner
- Lesson 12/112
- 6 min
In this group you learned to create NumPy arrays, use their properties, and index them.
Summary: NumPy basics
What you learned in this group:
- Creating arrays with
np.array,arange,linspace,zeros. - Inspecting arrays with
shape,ndim,size,dtype. - Selecting with
a[row, column], slices and boolean masks. - Changing shape with
reshape,flatten,.T.
X = np.arange(12).reshape(4, 3)
print(X[:, 0]) # first feature
print(X[X[:, 0] > 3]) # rows whose first feature is above 3Element-wise operations and vectorisation are the key to fast, short ML code.
Practice All together
Turn the numbers 0-11 into a 4×3 matrix. Select the last column, filter the values above 5 with a boolean mask and print them.
Starter code
import numpy as np
# buraya yazSample solution
import numpy as np
X = np.arange(12).reshape(4, 3)
son = X[:, -1]
print(son[son > 5])Expected output
[ 8 11]
Questions and answers
Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.
1 What does <code>np.arange(12).reshape(4, 3)[:, 0]</code> give?
- 0
- [0 3 6 9] Correct answer
- [0 1 2]
- [0 4 8]
Explanation: The rows are [0 1 2], [3 4 5], [6 7 8], [9 10 11]; the first element of each is taken.
2 Compute the sum of all elements.
Question
toplam = X.____()
Answer
toplam = X.sum()Explanation: sum adds all elements when no axis is given.
3 Match the tools with their roles.
| Term | Meaning |
|---|---|
linspace |
Evenly spaced values |
astype |
Type conversion |
reshape |
Changing shape |
x[x > 0] |
Conditional selection |