**Why Slicing for Insert/Delete:** Demonstrates understanding of list immutability patterns. lst[:i] + [x] + lst[i:] creates new list—useful in functional style or when you need to preserve original. **Complexity:** O(n) for both—slicing copies. For frequent mid-list ops, list...
This easy-level Python/Coding question appears frequently in data engineering interviews at companies like JIO. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (etl, python) will help you answer variations of this question confidently.
Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.
Why Slicing for Insert/Delete: Demonstrates understanding of list immutability patterns. lst[:i] + [x] + lst[i:] creates new list—useful in functional style or when you need to preserve original.
Complexity: O(n) for both—slicing copies. For frequent mid-list ops, list is wrong structure—use deque (ends) or linked structure (mid).
Production: In ETL, we rarely insert/delete by index—we filter (list comp) or use DataFrame drop/assign. This exercise is for interview fluency.
def insert_at(lst, i, x):
return lst[:i] + [x] + lst[i:]
def delete_at(lst, i):
return lst[:i] + lst[i+1:]
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked Python/Coding interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.