構造化配列

import numpy as np

# Define structured dtype
dt = np.dtype([('name', 'U10'), ('age', 'i4'), ('salary', 'f8')])

# Create array
employees = np.array([
    ('Alice', 30, 75000.0),
    ('Bob', 25, 62000.0),
    ('Charlie', 35, 85000.0),
    ('Diana', 28, 71000.0)
], dtype=dt)

# Access fields
print(employees['name'])     # ['Alice' 'Bob' 'Charlie' 'Diana']
print(employees['salary'])   # [75000. 62000. 85000. 71000.]

# Record array (attribute access)
emp_rec = np.rec.array(employees)
print(emp_rec.name)          # ['Alice' 'Bob' 'Charlie' 'Diana']
print(emp_rec.salary)        # [75000. 62000. 85000. 71000.]

# Filtering
print(employees[employees['salary'] > 70000]['name'])
# ['Alice' 'Charlie']

# Sorting
sorted_emp = np.sort(employees, order='salary')
print(sorted_emp['name'])  # ['Bob' 'Diana' 'Alice' 'Charlie']

> Output: Run NumPy 2.x in your local Python environment to see the ndarray output. The Piston server does not have NumPy pre-installed — install it locally (`pip install numpy`) and compare. Actual values may vary by NumPy version and random seed.

import numpy as np

dt = np.dtype([('product', 'U15'), ('price', 'f8'), ('quantity', 'i4')])

inventory = np.array([
    ('Widget', 9.99, 100),
    ('Gadget', 24.99, 50),
    ('Doohickey', 4.99, 200),
    ('Thingamajig', 14.99, 75)
], dtype=dt)

print("Products:", inventory['product'])
print("Prices:", inventory['price'])
print("Total value:", np.sum(inventory['price'] * inventory['quantity']))

import numpy as np

dt = np.dtype([('name', 'U15'), ('age', 'i4'), ('salary', 'f8')])
employees = np.array([
    ('Alice', 30, 75000), ('Bob', 25, 62000),
    ('Charlie', 35, 85000), ('Diana', 28, 71000),
    ('Eve', 45, 95000), ('Frank', 32, 78000)
], dtype=dt)

# Filter: salary > 70000 AND age < 40
mask = (employees['salary'] > 70000) & (employees['age'] < 40)
filtered = employees[mask]
print("Filtered:", filtered['name'])

# Average salary by age group
young = employees[employees['age'] < 30]
print("Young avg salary:", young['salary'].mean())

import numpy as np

dt = np.dtype([('city', 'U15'), ('population', 'i4'), ('area', 'f8')])
cities = np.array([
    ('Tokyo', 13960000, 2194), ('Delhi', 16780000, 1484),
    ('Shanghai', 24870000, 6341), ('Mumbai', 12440000, 603),
    ('Beijing', 21540000, 16411)
], dtype=dt)

# Sort by population descending
by_pop = np.sort(cities, order='population')[::-1]
print("By population:", by_pop['city'])

# Sort by density (population / area)
density = cities['population'] / cities['area']
dense_idx = np.argsort(-density)
print("By density:", cities['city'][dense_idx])