python Interview Questions

(4 questions)

Q1. Remove duplicates from a list (order doesn't matter vs order matters)

“Removing duplicates depends on whether we need to preserve the original order or not.”

Order doesn’t matter (fastest approach)
- Use set(), which automatically removes duplicates
- But it does not preserve order
```
nums = [1, 2, 2, 3, 4, 4]
unique_nums = list(set(nums))
```
Order matters (preserve original order)
- Using dict.fromkeys() (best modern approach)
  - Removes duplicates while keeping order (Python 3.7+)
```
unique_nums = list(dict.fromkeys(nums))
```
- Using loop (interview-friendly approach)
  - Manually check and append only unique values
```
unique_nums = []
for n in nums:
    if n not in unique_nums:
        unique_nums.append(n)
```

“So in simple terms, if order doesn’t matter I use set(), and if order matters I prefer dict.fromkeys() or a loop depending on the situation.”

Q2. How do you iterate and print all key-value pairs in a dictionary?

“To iterate over key-value pairs in a dictionary, the most common and preferred way is using the .items() method.”

Using .items() (best approach)

Returns both key and value together
We can directly unpack them in the loop

data = {"name": "Prabhu", "age": 25, "city": "Bangalore"}

for key, value in data.items():
    print(key, value)

Using .keys() (less preferred)
- Iterates over keys, then access values manually
```
for key in data.keys():
    print(key, data[key])
```
Default iteration (same as keys)
- Directly iterates over keys
```
for key in data:
    print(key, data[key])
```

“So in interviews, I prefer .items() because it’s more readable and directly gives both key and value.”

Q3. Explain lambda functions with examples

“A lambda function is a small anonymous function written in a single line, mainly used for short and simple operations.”

Syntax
```
lambda arguments: expression
```

Simple example

add = lambda a, b: a + b
print(add(2, 3))

Common use cases

With map() → apply operation to each element

nums = [1, 2, 3, 4]
squares = list(map(lambda x: x * x, nums))

With filter() → filter elements based on condition

nums = [1, 2, 3, 4, 5]
even = list(filter(lambda x: x % 2 == 0, nums))

With sorted() → custom sorting

data = [(1, 3), (2, 1), (4, 2)]
sorted_data = sorted(data, key=lambda x: x[1])

“In simple terms, lambda functions are one-line functions without a name, used for quick operations where defining a full function is not required.”

Q4. How do you read and write JSON and CSV files in Python?

“In Python, we use built-in libraries like json and csv, and in real projects, pandas is the most commonly used approach.”

JSON files

Read JSON

import json

with open("data.json", "r") as file:
    data = json.load(file)

Write JSON

import json

data = {"name": "Rahul", "age": 25}
with open("data.json", "w") as file:
    json.dump(data, file)

CSV files

Read CSV

import csv

with open("data.csv", "r") as file:
    reader = csv.reader(file)
    for row in reader:
        print(row)

Write CSV

import csv

data = [
    ["name", "age"],
    ["Rahul", 25],
    ["Amit", 30]
]

with open("data.csv", "w", newline="") as file:
    writer = csv.writer(file)
    writer.writerows(data)

Using pandas (most common in Data Engineering)

Easier and more efficient for real-world use

import pandas as pd

# Read
df = pd.read_csv("data.csv")
df_json = pd.read_json("data.json")

# Write
df.to_csv("output.csv", index=False)
df.to_json("output.json", orient="records")

“So in simple terms, for JSON we use json.load() and json.dump(), for CSV we use csv.reader() and csv.writer(), and in real projects we mostly use pandas because it’s more convenient and powerful.”