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Array properties

NumPy arrays have properties like shape, size, ndim, and dtype.

shape, ndim, size

Every array has attributes describing its structure:

a = np.array([[1, 2, 3],
              [4, 5, 6]])
print(a.shape)   # (2, 3)  rows, columns
print(a.ndim)    # 2       number of dimensions
print(a.size)    # 6       total elements

Most ML bugs are shape mismatches; when something goes wrong, the first thing to do is print shape.

dtype

dtype tells the type of the elements. Integers become int64 and decimals float64; in a mixed list NumPy converts everything to decimals:

print(np.array([1, 2]).dtype)      # int64
print(np.array([1, 2.5]).dtype)    # float64
x = np.array([1, 2]).astype(np.float32)
print(x.dtype)                     # float32

Deep learning often uses float32 to save memory; it is converted with astype.

Practice Inspect the array

Print the shape, ndim and size of array a in a single print, separated by spaces.

Starter code

model.py
import numpy as np

a = np.ones((3, 4, 2))

# buraya yaz

Sample solution

model.py
import numpy as np

a = np.ones((3, 4, 2))

print(a.shape, a.ndim, a.size)

Expected output

(3, 4, 2) 3 24

Questions and answers

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

1 What is <code>np.zeros((4, 5)).size</code>?

  • 20 Correct answer
  • 9
  • 2
  • (4, 5)

Explanation: size is the product of all dimensions: 4 × 5 = 20.

2 Convert the array to float32.

Question 
x = a.____(np.float32)
Answer 
x = a.astype(np.float32)

Explanation: astype returns a copy in the new type; the original array is unchanged.

3 Match the attributes.

Term Meaning
shape Tuple of dimensions
ndim Number of dimensions
size Total elements
dtype Element type

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