The Problem
When working with large datasets from public APIs, such as the JSONPlaceholder Todos API, traditional data processing approaches can become complex and inefficient, leading to hard-to-maintain codebases and decreased productivity.
Step 1 — Introduction to F-Strings
This step addresses the issue of string formatting and concatenation in Python, which can become cumbersome when dealing with multiple variables and data types. F-strings offer a more readable and efficient way to format strings.
todo_title = "delectus aut autem"
user_id = 1
formatted_string = f"Todo {todo_title} belongs to user {user_id}"
print(formatted_string)
This code snippet demonstrates how to use f-strings to format a string with variables, resulting in a more readable and maintainable code.
Step 2 — Structural Pattern Matching Basics
This step introduces structural pattern matching, a feature that simplifies conditional statements and improves code readability. We will use it to filter todos based on their completion status.
def filter_todos(todos):
completed_todos = []
for todo in todos:
match todo["completed"]:
case True:
completed_todos.append(todo)
case False:
pass
return completed_todos
This code snippet illustrates the use of structural pattern matching to filter a list of todos based on their completion status.
Step 3 — Combining F-Strings and Structural Pattern Matching
In this step, we will combine f-strings and structural pattern matching to create an efficient and readable data processing pipeline. We will use f-strings to format the results and structural pattern matching to filter completed todos.
def process_todos(todos):
completed_todos = []
for todo in todos:
match todo["completed"]:
case True:
formatted_string = f"Todo {todo['title']} belongs to user {todo['userId']}"
completed_todos.append(formatted_string)
case False:
pass
return completed_todos
This code snippet demonstrates how to combine f-strings and structural pattern matching to process a list of todos and return a formatted list of completed todos.
Step 4 — Handling Errors and Edge Cases
This step discusses how to handle errors and edge cases when working with public APIs and large datasets. We will use try-except blocks to handle API request errors and data parsing exceptions.
import requests
try:
response = requests.get("https://jsonplaceholder.typicode.com/todos")
data = response.json()
except requests.exceptions.RequestException as e:
print(f"Request error: {e}")
except ValueError as e:
print(f"Data parsing error: {e}")
This code snippet shows how to handle API request errors and data parsing exceptions when fetching data from the JSONPlaceholder Todos API.
Complete Script
The full runnable script combining all steps:
#!/usr/bin/env python3
import requests
def load_data():
try:
response = requests.get("https://jsonplaceholder.typicode.com/todos")
data = response.json()
return data
except requests.exceptions.RequestException as e:
print(f"Request error: {e}")
return []
except ValueError as e:
print(f"Data parsing error: {e}")
return []
def process_todos(todos):
completed_todos = []
for todo in todos:
match todo["completed"]:
case True:
formatted_string = f"Todo {todo['title']} belongs to user {todo['userId']}"
completed_todos.append(formatted_string)
case False:
pass
return completed_todos
if __name__ == "__main__":
data = load_data()
result = process_todos(data)
for todo in result:
print(todo)
Expected Output
When you run the script, you should see a list of formatted strings representing the completed todos, including their titles and user IDs.
What I'd Change
In a real-world scenario, I would consider adding more robust error handling and logging mechanisms to ensure that the script can handle a wide range of potential errors and exceptions. Additionally, I would explore using more advanced data processing techniques, such as data streaming or parallel processing, to improve the performance and scalability of the script.
