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Creating arrays

NumPy arrays are created from Python lists using the np.array() function.

Creating from lists and ranges

The most common ways:

np.array([[1, 2], [3, 4]])   # from a list
np.arange(0, 10, 2)           # [0 2 4 6 8], end excluded
np.linspace(0, 1, 5)          # [0.   0.25 0.5  0.75 1.  ], end included

arange takes a step size, linspace takes an element count. linspace is handy for plot axes or evenly spaced trial values.

Pre-filled arrays

For arrays whose size is known but whose contents will be filled later:

np.zeros((2, 3))     # 2x3 zeros
np.ones(4)           # [1. 1. 1. 1.]
np.full((2, 2), 7)   # 7 everywhere
np.eye(3)            # 3x3 identity matrix

The size is given as a tuple ((rows, columns)). The default type is float64, which is why 1. appears in the output.

Practice Produce arrays

Create the array from 0 to 20 (exclusive) stepping by five with np.arange, and a 2-row, 2-column zero matrix with np.zeros. Print the array first, then the matrix's shape.

Starter code

model.py
import numpy as np

# buraya yaz

Sample solution

model.py
import numpy as np

a = np.arange(0, 20, 5)
z = np.zeros((2, 2))
print(a)
print(z.shape)

Expected output

[ 0  5 10 15]
(2, 2)

Questions and answers

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

1 What does <code>np.arange(1, 7, 2)</code> produce?

  • [1 2 3 4 5 6]
  • [1 3 5] Correct answer
  • [1 3 5 7]
  • [2 4 6]

Explanation: The end value (7) is excluded; it starts at 1 and steps by two.

2 Produce 11 evenly spaced values between 0 and 1.

Question 
x = np.____(0, 1, 11)
Answer 
x = np.linspace(0, 1, 11)

Explanation: linspace takes the element count and includes the end.

3 Match the functions with their results.

Term Meaning
np.zeros(3) [0. 0. 0.]
np.ones(2) [1. 1.]
np.arange(3) [0 1 2]
np.full(2, 5) [5 5]

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