NumPy Manipulating Structured Arrays
Structured arrays in NumPy allow you to store mixed data types like:
- Name (string)
- Age (integer)
- Salary (float)
But real-world data is not static — you often need to modify, update, filter, and sort it.
This is where manipulating structured arrays becomes important.
What is Structured Array Manipulation?
It means performing operations such as:
- Updating values
- Filtering data
- Sorting records
- Adding or modifying fields
Why Manipulate Structured Arrays?
- Clean and update datasets
- Extract useful information
- Organize data efficiently
- Prepare data for analysis or ML
- Work like a lightweight database
1. Creating a Structured Array
import numpy as np
data = np.array([
("Alice", 25, 50000),
("Bob", 30, 60000),
("Charlie", 28, 55000)
], dtype=[("name", "U10"), ("age", "i4"), ("salary", "i4")])
print(data)
2. Accessing Fields
print(data["name"])
print(data["age"])
Output
['Alice' 'Bob' 'Charlie']
[25 30 28]
3. Updating Values in Structured Arrays
data["salary"][0] = 52000
print(data)
Output
[('Alice', 25, 52000)
('Bob', 30, 60000)
('Charlie', 28, 55000)]
4. Updating Multiple Fields
data["age"] = data["age"] + 1
print(data)
5. Filtering Structured Arrays
filtered = data[data["age"] > 27]
print(filtered)
Output
[('Bob', 30, 60000)
('Charlie', 28, 55000)]
6. Filtering by Salary
high_salary = data[data["salary"] > 55000]
print(high_salary)
7. Sorting Structured Arrays
sorted_data = np.sort(data, order="age")
print(sorted_data)
Output
[('Alice', 25, 52000)
('Charlie', 28, 55000)
('Bob', 30, 60000)]
8. Sorting by Salary
sorted_salary = np.sort(data, order="salary")
print(sorted_salary)
9. Selecting Specific Records
print(data[0]) # First record
print(data[-1]) # Last record
10. Modifying Strings in Structured Arrays
data["name"] = np.char.upper(data["name"])
print(data)
11. Adding New Values (Indirect Method)
Structured arrays are fixed-size, but we can use append:
new_data = np.append(data, [("David", 35, 70000)])
print(new_data)
12. Real-World Example: Employee Update System
import numpy as np
employees = np.array([
("John", 40, 80000),
("Emma", 29, 65000),
("Mike", 33, 72000)
], dtype=[("name", "U10"), ("age", "i4"), ("salary", "i4")])
employees["salary"][1] = 68000
print(employees)
13. Real-World Example: Student Records
students = np.array([
("A", 90),
("B", 75),
("C", 88)
], dtype=[("name", "U10"), ("marks", "i4")])
students["marks"] = students["marks"] + 5
print(students)
Common Manipulation Operations
| Operation | Description |
|---|---|
| Update | Change values |
| Filter | Select data |
| Sort | Arrange records |
| Access | Read fields |
| Modify | Transform data |
Advantages of Manipulating Structured Arrays
- Easy data cleaning
- Fast operations
- Column-based control
- Useful in analytics
- Works like database tables
Summary
Manipulating structured arrays in NumPy allows you to efficiently update, filter, sort, and manage complex datasets. This makes it highly useful for real-world data processing.
This functionality is part of NumPy and widely used in applications built with Python.
Conclusion
Structured array manipulation is essential for handling real-world data. It provides powerful tools for updating and organizing structured datasets in Python efficiently.
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