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What is NumPy?

NumPy is the fundamental library for numerical computing in Python, providing multi-dimensional arrays (ndarray).

What is NumPy?

NumPy is the foundation of numerical computing in Python. At its centre is the ndarray: a multi-dimensional array that keeps numbers of the same type in contiguous memory. scikit-learn, pandas and PyTorch build their data on this structure.

import numpy as np
a = np.array([1, 2, 3])
print(a * 2)      # [2 4 6]
print(a + a)      # [2 4 6]

With a Python list, [1, 2, 3] * 2 repeats the list; in NumPy the operation is applied to every element.

Why is it fast?

NumPy's loops are written in C. Computing the whole array in one operation instead of writing a loop in Python is called vectorisation:

x = np.arange(1_000_000)
# slow: sum(v * v for v in x)
print((x * x).sum())   # fast, one line

The rule in ML code is simple: before writing a for over an array's elements, check whether NumPy has an equivalent.

Practice Vectorise

Add 20% VAT to every value in the fiyat array (multiply by 1.2) and print the result. Don't use a loop.

Starter code

model.py
import numpy as np

fiyat = np.array([100, 250, 40])

# buraya yaz

Sample solution

model.py
import numpy as np

fiyat = np.array([100, 250, 40])

print(fiyat * 1.2)

Expected output

[120. 300.  48.]

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 What does <code>np.array([1, 2]) * 3</code> give?

  • 9
  • An error
  • [3 6] Correct answer
  • [1 2 1 2 1 2]

Explanation: NumPy operations are element-wise; each element is multiplied by 3.

2 Import NumPy under its usual short name.

Question 
import numpy as ____
Answer 
import numpy as np

Explanation: The community convention is the np alias; all documentation uses it.

3 Match the concepts.

Term Meaning
ndarray Multi-dimensional array
vectorization Loop-free array operation
a * 2 Element-wise multiplication
np Usual alias

All lessons in Beginner

1 What is ML?

  1. What is machine learning?
  2. Supervised and unsupervised
  3. Data and model
  4. Training and testing
  5. Feature and label notation
  6. Summary - ML fundamentals

2 NumPy basics

  1. What is NumPy?
  2. Creating arrays
  3. Array properties
  4. Array indexing
  5. Reshaping arrays
  6. Summary - NumPy basics

3 Pandas basics

  1. What is Pandas?
  2. Creating a DataFrame
  3. Column operations
  4. Selecting and filtering data
  5. Viewing first rows
  6. Summary - Pandas basics

4 Data preprocessing

  1. Data cleaning
  2. Missing data
  3. Data types
  4. Label encoding
  5. Data splitting
  6. Summary - Data preprocessing

5 Statistics basics

  1. Mean
  2. Median
  3. Standard deviation
  4. Variance
  5. Correlation
  6. Summary - Statistics

6 Linear algebra

  1. Vectors
  2. Matrices
  3. Matrix multiplication
  4. Transpose
  5. Identity matrix
  6. Summary - Linear algebra

7 Project: Data analysis

  1. Loading data
  2. Cleaning data
  3. Computing statistics
  4. Printing results

8 Regression

  1. What is regression?
  2. Linear regression
  3. Model training
  4. Prediction
  5. Error measurement
  6. Summary - Regression

9 KNN

  1. What is KNN?
  2. The K parameter
  3. Distance measurement
  4. Classification
  5. Model training
  6. Summary - KNN

10 Decision trees

  1. What is a decision tree?
  2. Splitting criterion
  3. Depth
  4. Classification
  5. Overfitting
  6. Summary - Decision trees

11 Classification

  1. What is classification?
  2. Binary classification
  3. Multi-class
  4. Accuracy
  5. Confusion matrix
  6. Summary - Classification

12 Model evaluation

  1. Train-test split
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Model evaluation

13 Project: Classification

  1. Preparing data
  2. Training model
  3. Prediction
  4. Evaluation

14 Scaling

  1. What is scaling?
  2. StandardScaler
  3. MinMaxScaler
  4. When to use?
  5. Pipeline
  6. Summary - Scaling

15 Feature engineering

  1. What is a feature?
  2. Feature selection
  3. Feature extraction
  4. Categorical features
  5. Numerical features
  6. Summary - Feature engineering

16 Model selection

  1. What is model selection?
  2. Cross-validation
  3. Grid search
  4. Score comparison
  5. Hyperparameter
  6. Summary - Model selection

17 Project: Regression

  1. Preparing data
  2. Training model
  3. Prediction
  4. Error measurement

18 Metrics

  1. What is a metric?
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Metrics

19 Data visualization

  1. What is visualization?
  2. Scatter plot
  3. Histogram
  4. Line plot
  5. Box plot
  6. Summary - Visualization

20 Beginner final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4