Do You REALLY know Python? Quant Dev Mock Interview
Can you survive the Python Interview Iceberg? In this video, I break down real Quant Developer interview questions, ranking them from basic data structures to obscure internal mechanics.
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00:00 Intro
00:21 sep vs end in print()
print("A", "B", sep="-", end=" ")
Output: A-B
01:01 Print latency
02:17 == versus id()
a = [1, 2, 3]
b = [1, 2, 3]
print(a == b) # True (values match)
print(a is b) # False (different memory addresses)
03:13 What is an Iterable?
04:02 How does an Iterable end?
04:45 Speed up Python loops
squares = [x**2 for x in range(10)]
05:51 List complexities
07:06 Variadic arguments
def func(*args, **kwargs):
for arg in args:
print(arg)
07:54 Level 2: Classes, Modules, Dictionaries
07:59 Class vs Instance attributes
class Dog:
species = "Canis" # Class attribute
def init(self, name):
self.name = name # Instance attribute
08:36 slots
class Ca:
_slots_ = 'foo', 'bar'
09:14 How Garbage Collection works
10:48 Definition of a Module
11:09 Import time vs Runtime
12:10 Dictionary implementation
12:53 d.get(k) vs d[k]
val = d.get("key", "default_value")
val = d["key"]
13:08 List Comprehension vs Generator Expression
List Comp
lst = [x*2 for x in range(1000)]
Gen Exp
gen = (x*2 for x in range(1000))
13:35 Can you reset a generator?
13:51 Level 3: Decorators, Context Managers, Metaclasses
14:30 Writing a custom decorator
import functools
def my_decorator(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
print("Before")
result = func(*args, **kwargs)
print("After")
return result
return wrapper
15:33 Writing a custom Context Manager
class FileManager:
def enter(self):
self.f = open("file.txt", "w")
return self.f
def exit(self, exc_type, exc_val, exc_tb):
self.f.close()
15:59 Context Manager guarantees & exceptions
16:33 Metaclasses defined
17:28 Level 4: AsyncIO, Descriptors, ABCs, GIL
18:22 Abstract Base Classes (ABCs)
from abc import ABC, abstractmethod
class Shape(ABC):
@abstractmethod
def area(self):
pass
18:41 The Global Interpreter Lock (GIL)
19:39 Cooperative Concurrency vs Preemptive Scheduling
20:02 Descriptors & @property
class Circle:
def init(self, radius):
self._radius = radius
@property
def radius(self):
return self._radius
20:26 Goose Typing
from collections.abc import Sized
class MyList:
def len(self): return 5
print(isinstance(MyList(), Sized)) # True (Goose Typing)
21:48 Level 5: Bytecode & CPython Internals
22:14 CPython memory management
23:30 Inspecting Bytecode
import dis
def hello(): print("Hello")
dis.dis(hello)
23:51 CPython vs other implementations
24:40 What does "Pythonic" mean?
25:56 Const sets and const dictionaries
Immutable Set
f_set = frozenset([1, 2, 3])
Read-only Dict proxy
from types import MappingProxyType
d = {"a": 1}
read_only = MappingProxyType(d)
27:11 Controlling memory allocation (new)
class Singleton:
_instance = None
def new(cls):
if cls._instance is None:
cls._instance = super().new(cls)
return cls._instance
27:53 Feedback
29:39 Writing a Memoize decorator (@cache)
from functools import lru_cache
@lru_cache(maxsize=None)
def fib(n):
if n == 1:
return n
return fib(n-1) + fib(n-2)
31:38 Is Set order deterministic? (Salting)
32:24 Interview timeline
32:41 Resources for studying Python
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Reference
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