data_type_question.md

title	tags
Data Type Operations	python data-types strings lists dictionaries sets type-conversion

Problem Statement

Implement a function transform_data(num, text, items) that performs the following operations:

1. Takes three parameters:

   • num: An integer or float
   • text: A string
   • items: A list of elements

2. The function should:

   • Convert the number to its absolute value and round it to 2 decimal places
   • Convert the text to uppercase and remove any leading/trailing spaces
   • Convert the list into a set to remove duplicates, then back to a sorted list
   • Create a dictionary with three keys:
     • 'number': processed number
     • 'text': processed text
     • 'items': processed list

3. Return the resulting dictionary

Example:

Input: transform_data(-15.3267, "  Hello world  ", [1, 3, 2, 1, 4, 2])
Output: {'number': 15.33, 'text': 'HELLO WORLD', 'items': [1, 2, 3, 4]}

Solution

<prefix>
def transform_data(num, text, items):
</prefix>
<template>
<sol>
    # Process the number
    processed_num = round(abs(num), 2)
    
    # Process the text
    processed_text = text.strip().upper()
    
    # Process the list
    processed_items = sorted(list(set(items)))
    
    # Create and return the dictionary
    return {
        'number': processed_num,
        'text': processed_text,
        'items': processed_items
    }
</sol>
</template>
<suffix>
num = float(input())
text = input()
items = eval(input())
result = transform_data(num, text, items)
print(result)
</suffix>
<suffix_invisible>
</suffix_invisible>

Public Test Cases

Input 1

-15.3267
  Hello world  
[1, 3, 2, 1, 4, 2]

Output 1

{'number': 15.33, 'text': 'HELLO WORLD', 'items': [1, 2, 3, 4]}

Input 2

42.0
   Python  
[5, 5, 5, 5]

Output 2

{'number': 42.0, 'text': 'PYTHON', 'items': [5]}

Private Test Cases

Input 1

-0.0056
  ALREADY upper  
[10, 20, 30, 20, 10]

Output 1

{'number': 0.01, 'text': 'ALREADY UPPER', 'items': [10, 20, 30]}

Input 2

789.98765
   spaces   everywhere   
[]

Output 2

{'number': 789.99, 'text': 'SPACES   EVERYWHERE', 'items': []}

Input 3

-123456.789
NoSpaces
[1, 2, 3, 2, 1]

Output 3

{'number': 123456.79, 'text': 'NOSPACES', 'items': [1, 2, 3]}

Input 4

0.0
   
[9, 8, 7, 8, 9]

Output 4

{'number': 0.0, 'text': '', 'items': [7, 8, 9]}