NumPy Creating Structured Arrays
In real-world data, we often deal with different types of information together.
For example:
- Name (string)
- Age (integer)
- Salary (float)
NumPy structured arrays allow us to store all these different types in a single array.
What are Structured Arrays?
Structured arrays are NumPy arrays with custom data types (fields).
Each element can have multiple named attributes, similar to a database record or table row.
Why Use Structured Arrays?
- Store mixed data types
- Easy data organization
- Works like a mini database
- Useful in data science
- Efficient memory usage
1. Basic Structured Array Creation
import numpy as np
data = np.array([
("Alice", 25, 50000.0),
("Bob", 30, 60000.0),
("Charlie", 28, 55000.0)
], dtype=[("name", "U10"), ("age", "i4"), ("salary", "f4")])
print(data)
Output
[('Alice', 25, 50000.)
('Bob', 30, 60000.)
('Charlie', 28, 55000.)]
2. Understanding dtype (Data Types)
| Field | Type | Meaning |
|---|---|---|
| name | U10 | String (Unicode up to 10 chars) |
| age | i4 | Integer |
| salary | f4 | Float |
3. Creating Empty Structured Array
import numpy as np
dtype = [("name", "U10"), ("age", "i4"), ("salary", "f4")]
data = np.zeros(3, dtype=dtype)
print(data)
Output
[('', 0, 0.) ('', 0, 0.) ('', 0, 0.)]
4. Creating Structured Array Using np.array()
import numpy as np
data = np.array([
("John", 40, 80000),
("Emma", 29, 65000)
], dtype=[("name", "U10"), ("age", "i4"), ("salary", "i4")])
print(data)
5. Creating Structured Array Using np.zeros()
import numpy as np
dtype = [("product", "U10"), ("price", "f4")]
products = np.zeros(3, dtype=dtype)
products["product"] = ["Pen", "Book", "Laptop"]
products["price"] = [1.5, 5.0, 1000.0]
print(products)
6. Creating Structured Array Using np.array with Dictionaries
import numpy as np
data = np.array([
{"name": "Alice", "age": 25, "salary": 50000},
{"name": "Bob", "age": 30, "salary": 60000}
])
print(data)
7. Accessing Structured Array Fields
print(data["name"])
print(data["age"])
8. Creating Structured Array for Students
import numpy as np
students = np.array([
("A", 90),
("B", 75),
("C", 88)
], dtype=[("name", "U10"), ("marks", "i4")])
print(students)
9. Updating Structured Array Values
students["marks"][0] = 95
print(students)
10. Real-World Example: Employee Records
import numpy as np
employees = np.array([
("John", 40, 80000),
("Emma", 29, 65000),
("Mike", 33, 72000)
], dtype=[("name", "U10"), ("age", "i4"), ("salary", "i4")])
print(employees)
11. Real-World Example: Product Inventory
import numpy as np
inventory = np.array([
("Laptop", 10, 900.0),
("Mouse", 50, 20.0),
("Keyboard", 30, 45.0)
], dtype=[("item", "U10"), ("stock", "i4"), ("price", "f4")])
print(inventory)
Key Features of Structured Arrays
| Feature | Description |
|---|---|
| dtype | Defines data structure |
| fields | Named columns |
| mixed types | string, int, float |
| indexing | field-based access |
Advantages of Structured Arrays
- Store multiple data types
- Easy data management
- Similar to database tables
- Efficient memory usage
- Useful in analytics and ML
Summary
NumPy structured arrays allow you to create and manage complex datasets with multiple data types in a single array. They behave like lightweight database tables and are very useful in data science.
This functionality is part of NumPy and is widely used in applications built with Python.
Conclusion
Creating structured arrays in NumPy is essential for handling real-world data. It simplifies data storage, access, and processing, making it a powerful tool for data science and machine learning.
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