Accessing fields by name in NumPy - Mini Project: Build & Apply

Accessing Fields by Name in NumPy Structured Arrays

📖 Scenario: You work in a small store and keep track of products using a table with columns for product name, price, and quantity.

🎯 Goal: You will create a structured NumPy array to store product data, then access the price field by its name to see the prices of all products.

📋 What You'll Learn

Create a NumPy structured array with fields 'name', 'price', and 'quantity'.

Use a configuration variable to select the field name 'price'.

Access the 'price' field from the structured array using the field name.

Print the prices of all products.

💡 Why This Matters

🌍 Real World

Stores and businesses often keep product data in tables with named columns. Accessing fields by name helps quickly get specific information like prices or quantities.

💼 Career

Data scientists and analysts use structured arrays or tables to organize data. Knowing how to access fields by name is essential for data cleaning, analysis, and reporting.

1

Create a NumPy structured array with product data

Import NumPy as np. Create a structured array called products with three entries. Each entry should have fields: 'name' (string of length 10), 'price' (float), and 'quantity' (integer). Use these exact values: ('apple', 0.5, 10), ('banana', 0.3, 20), ('orange', 0.8, 15).

NumPy

import numpy as np
# Create the structured array 'products' with the specified fields and values
# Your code here

Hint

Use np.array with a list of tuples and specify dtype as a list of tuples with field names and types.

2

Set a variable for the field name to access

Create a variable called field_name and set it to the string 'price'. This will help us select the price field later.

NumPy

import numpy as np
products = np.array([
    ('apple', 0.5, 10),
    ('banana', 0.3, 20),
    ('orange', 0.8, 15)
], dtype=[('name', 'U10'), ('price', 'f4'), ('quantity', 'i4')])
# Create the variable 'field_name' and set it to 'price'
# Your code here

Hint

Just assign the string 'price' to the variable field_name.

3

Access the price field using the field name variable

Use the variable field_name to access the price field from the products array. Store the result in a variable called prices.

NumPy

import numpy as np
products = np.array([
    ('apple', 0.5, 10),
    ('banana', 0.3, 20),
    ('orange', 0.8, 15)
], dtype=[('name', 'U10'), ('price', 'f4'), ('quantity', 'i4')])
field_name = 'price'
# Access the 'price' field using 'field_name' and store it in 'prices'
# Your code here

Hint

Use products[field_name] to get the array of prices.

4

Print the prices of all products

Print the variable prices to display the prices of all products.

NumPy

import numpy as np
products = np.array([
    ('apple', 0.5, 10),
    ('banana', 0.3, 20),
    ('orange', 0.8, 15)
], dtype=[('name', 'U10'), ('price', 'f4'), ('quantity', 'i4')])
field_name = 'price'
prices = products[field_name]
# Print the prices
# Your code here

Hint

Use print(prices) to show the prices.

Practice

1. What is the correct way to access the field named 'age' from a NumPy structured array data?

easy

A. data['age']

B. data.age()

C. data[age]

D. data.get('age')

Solution

1. Step 1: Understand structured array field access

   In NumPy, fields in structured arrays are accessed using square brackets with the field name as a string.

2. Step 2: Identify correct syntax for field access

   The syntax data['age'] correctly accesses the 'age' field. Other options use incorrect methods or syntax.

3. Final Answer:

   data['age'] -> Option A

4. Quick Check:

   Field access uses square brackets with field name [OK]

Hint: Use square brackets with field name as string [OK]

Common Mistakes:

• Using unquoted field name like data[age]
• Calling field as a method like data.age()
• Using data.get() which is not valid for structured arrays

2. Which of the following is the correct syntax to create a NumPy structured array with fields 'name' (string) and 'score' (integer)?

easy

A. np.array([('Alice', 90), ('Bob', 85)], dtype=[('name', 'U10'), ('score', 'i4')])

B. np.array([('Alice', 90), ('Bob', 85)], dtype={name: 'U10', score: 'i4'})

C. np.array([('Alice', 90), ('Bob', 85)], dtype=[{name: 'U10'}, {score: 'i4'}])

D. np.array([('Alice', 90), ('Bob', 85)], dtype=('name', 'U10', 'score', 'i4'))

Solution

1. Step 1: Understand dtype format for structured arrays

   The dtype should be a list of tuples, each tuple with field name and data type.

2. Step 2: Match correct dtype syntax

   np.array([('Alice', 90), ('Bob', 85)], dtype=[('name', 'U10'), ('score', 'i4')]) uses the correct list of tuples format: [('name', 'U10'), ('score', 'i4')]. Other options use incorrect dtype formats.

3. Final Answer:

   np.array([('Alice', 90), ('Bob', 85)], dtype=[('name', 'U10'), ('score', 'i4')]) -> Option A

4. Quick Check:

   dtype as list of (name, type) tuples [OK]

Hint: Use list of (field, type) tuples for dtype [OK]

Common Mistakes:

• Using dict instead of list of tuples for dtype
• Passing dtype as a flat tuple instead of list
• Incorrect nested dict inside dtype list

3. Given the structured array arr = np.array([(1, 2.5), (3, 4.5)], dtype=[('x', 'i4'), ('y', 'f4')]), what is the output of arr['y']?

medium

A. [1. 3.]

B. [2.5 4.5]

C. [(1, 2.5) (3, 4.5)]

D. Error: field 'y' not found

Solution

1. Step 1: Understand the structured array fields

   The array has two fields: 'x' (integers) and 'y' (floats). The values for 'y' are 2.5 and 4.5.

2. Step 2: Access the 'y' field values

   Using arr['y'] returns an array of the 'y' values: [2.5, 4.5].

3. Final Answer:

   [2.5 4.5] -> Option B

4. Quick Check:

   arr['y'] returns float values [OK]

Hint: Access field returns array of that field's values [OK]

Common Mistakes:

• Confusing field 'x' values with 'y'
• Expecting full tuples instead of single field array
• Assuming error due to wrong field name

4. What is wrong with this code snippet?

arr = np.array([(1, 2), (3, 4)], dtype=[('id', 'i4'), ('b', 'i4')])
print(arr.a)

medium

A. It raises a TypeError because dtype is incorrect.

B. It raises a SyntaxError due to missing quotes around field names.

C. It prints the array correctly without errors.

D. It raises an AttributeError because fields are accessed with brackets, not dot notation.

Solution

1. Step 1: Check field access method

   NumPy structured array fields must be accessed using square brackets with the field name as a string, not dot notation.

2. Step 2: Identify error from dot notation

   Using arr.a causes AttributeError because 'a' is not an attribute but a field name.

3. Final Answer:

   It raises an AttributeError because fields are accessed with brackets, not dot notation. -> Option D

4. Quick Check:

   Use arr['a'], not arr.a [OK]

Hint: Use brackets, not dot, to access fields [OK]

Common Mistakes:

• Using dot notation to access fields
• Assuming dtype syntax error
• Expecting code to print without error

5. You have a structured array data with fields 'name' (string), 'age' (int), and 'score' (float). How do you create a new array containing only the 'name' and 'score' fields?

hard

A. data['name']['score']

B. data[['name'], ['score']]

C. data[['name', 'score']]

D. data.get(['name', 'score'])

Solution

1. Step 1: Understand field selection syntax

   To select multiple fields, use a list of field names inside double square brackets: data[['field1', 'field2']].

2. Step 2: Apply correct syntax to select 'name' and 'score'

   Using data[['name', 'score']] returns a new structured array with only those fields.

3. Final Answer:

   data[['name', 'score']] -> Option C

4. Quick Check:

   Use double brackets with list of fields [OK]

Hint: Use double brackets with list of fields to select multiple [OK]

Common Mistakes:

• Chaining field accesses like data['name']['score']
• Passing separate lists for each field
• Using .get() method which does not exist