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80 Python Interview Questions.pdf
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- Covers frequently asked questions in Python interviews
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A huge cheat sheet for Python.pdf
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❔ Interview Question
What is the potential pitfall of using a mutable object (like a list or dictionary) as a default argument in a Python function?
Answer: A common pitfall is that the default argument is evaluated only once, when the function is defined, not each time it is called. If that default object is mutable, any modifications made to it in one call will persist and be visible in subsequent calls.
This can lead to unexpected and buggy behavior.
Incorrect Example (The Pitfall):
The Correct, Idiomatic Solution:
The standard practice is to use
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By: @DataScience4 ✨
What is the potential pitfall of using a mutable object (like a list or dictionary) as a default argument in a Python function?
Answer: A common pitfall is that the default argument is evaluated only once, when the function is defined, not each time it is called. If that default object is mutable, any modifications made to it in one call will persist and be visible in subsequent calls.
This can lead to unexpected and buggy behavior.
Incorrect Example (The Pitfall):
def add_to_list(item, my_list=[]):
my_list.append(item)
return my_list
# First call seems to work fine
print(add_to_list(1)) # Output: [1]
# Second call has unexpected behavior
print(add_to_list(2)) # Output: [1, 2] -- The list from the first call was reused!
# Third call continues the trend
print(add_to_list(3)) # Output: [1, 2, 3]
The Correct, Idiomatic Solution:
The standard practice is to use
None as the default and create a new mutable object inside the function if one isn't provided.def add_to_list_safe(item, my_list=None):
if my_list is None:
my_list = [] # Create a new list for each call
my_list.append(item)
return my_list
# Each call now works independently
print(add_to_list_safe(1)) # Output: [1]
print(add_to_list_safe(2)) # Output: [2]
print(add_to_list_safe(3)) # Output: [3]
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By: @DataScience4 ✨
❤2
Python tip:
Use f-strings for easy and readable string formatting.
Python tip:
Utilize list comprehensions for concise and efficient list creation.
Python tip:
Use
Python tip:
Use
Python tip:
Always use the
Python tip:
Use
Python tip:
Use
Python tip:
Employ
Python tip:
Use
Python tip:
Apply type hints to your code for improved readability, maintainability, and to enable static analysis tools.
#PythonTips #PythonProgramming #PythonForBeginners #PythonTricks #CodeQuality #Pythonic #BestPractices #LearnPython
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By: @DataScience4 ✨
Use f-strings for easy and readable string formatting.
name = "Alice"
age = 30
message = f"Hello, my name is {name} and I am {age} years old."
print(message)
Python tip:
Utilize list comprehensions for concise and efficient list creation.
numbers = [1, 2, 3, 4, 5]
squares = [x * x for x in numbers if x % 2 == 0]
print(squares)
Python tip:
Use
enumerate() to iterate over a sequence while also getting the index of each item.fruits = ["apple", "banana", "cherry"]
for index, fruit in enumerate(fruits):
print(f"{index}: {fruit}")
Python tip:
Use
zip() to iterate over multiple iterables in parallel.names = ["Alice", "Bob"]
ages = [25, 30]
for name, age in zip(names, ages):
print(f"{name} is {age} years old.")
Python tip:
Always use the
with statement when working with files to ensure they are properly closed, even if errors occur.with open("example.txt", "w") as f:
f.write("Hello, world!\n")
f.write("This is a test.")
# File is automatically closed herePython tip:
Use
*args to allow a function to accept a variable number of positional arguments.def sum_all(*args):
total = 0
for num in args:
total += num
return total
print(sum_all(1, 2, 3))
print(sum_all(10, 20, 30, 40))
Python tip:
Use
**kwargs to allow a function to accept a variable number of keyword arguments (as a dictionary).def display_info(**kwargs):
for key, value in kwargs.items():
print(f"{key}: {value}")
display_info(name="Bob", age=40, city="New York")
Python tip:
Employ
defaultdict from the collections module to simplify handling missing keys in dictionaries by providing a default factory.from collections import defaultdict
data = [("fruit", "apple"), ("vegetable", "carrot"), ("fruit", "banana")]
categorized = defaultdict(list)
for category, item in data:
categorized[category].append(item)
print(categorized)
Python tip:
Use
if __name__ == "__main__": to define code that only runs when the script is executed directly, not when imported as a module.def greet(name):
return f"Hello, {name}!"
if __name__ == "__main__":
print("Running directly as a script.")
print(greet("World"))
else:
print("This module was imported.")
Python tip:
Apply type hints to your code for improved readability, maintainability, and to enable static analysis tools.
def add(a: int, b: int) -> int:
return a + b
result: int = add(5, 3)
print(result)
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By: @DataScience4 ✨
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