What does
nonlocal do and where can it be used?Answer:
This is often used in closures to maintain and update state between calls to the nested function.
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π Tricky Python Interview Question
> What will this code output and why?
βQuestion: Why are list1 and list3 the same?
π Explanation:
Default arguments in Python are evaluated once β at function definition, not at each call.
So lst=[] is created once and preserved between calls if you don't explicitly pass your own list.
π§ What happens:
- extend_list(10) β uses the shared list [], now it is [10]
- extend_list(123, []) β creates a new list [123]
- extend_list('a') β again uses the shared list β [10, 'a']
π Result:
β How to fix:
If you want a new list created by default on each call, do this:
This is a classic Python interview trap β mutable default arguments.
It tests if you understand how default values and memory scope work.
https://t.iss.one/DataScienceQβοΈ
> What will this code output and why?
def extend_list(val, lst=[]):
lst.append(val)
return lst
list1 = extend_list(10)
list2 = extend_list(123, [])
list3 = extend_list('a')
print(list1, list2, list3)
βQuestion: Why are list1 and list3 the same?
π Explanation:
Default arguments in Python are evaluated once β at function definition, not at each call.
So lst=[] is created once and preserved between calls if you don't explicitly pass your own list.
π§ What happens:
- extend_list(10) β uses the shared list [], now it is [10]
- extend_list(123, []) β creates a new list [123]
- extend_list('a') β again uses the shared list β [10, 'a']
π Result:
[10, 'a'] [123] [10, 'a']β How to fix:
If you want a new list created by default on each call, do this:
def extend_list(val, lst=None):
if lst is None:
lst = []
lst.append(val)
return lst
This is a classic Python interview trap β mutable default arguments.
It tests if you understand how default values and memory scope work.
https://t.iss.one/DataScienceQ
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βοΈ Tricky Python Interview Question
> What will this code output and why?
β Question: Why does this code print 2, 2, 2 instead of 0, 1, 2?
π Explanation:
This is a classic example of late binding in Python closures. The lambda functions don't capture the value of
π§ What happens:
β’ The
β’ The
β’ When you call each function
π Result:
2
2
2
β How to fix:
You can force the lambda to capture the value of
This trick works because default arguments are evaluated when the function is defined, not when it's called. This will correctly print:
0
1
2
This is a common interview question that tests your understanding of closures, scope, and late binding in Python.
βββββββββββββββ
By: @DataScienceQ β¨
> What will this code output and why?
funcs = []
for i in range(3):
funcs.append(lambda: print(i))
for f in funcs:
f()
β Question: Why does this code print 2, 2, 2 instead of 0, 1, 2?
π Explanation:
This is a classic example of late binding in Python closures. The lambda functions don't capture the value of
i at each step of the loop. Instead, they all hold a reference to the same variable i.π§ What happens:
β’ The
for loop completes. By the end of the loop, the variable i holds the value 2.β’ The
funcs list contains three functions, but each one is defined to print the value of whatever i is at the time of execution.β’ When you call each function
f(), it looks up the current value of i in its enclosing scope, which is 2.π Result:
2
2
2
β How to fix:
You can force the lambda to capture the value of
i at the time of definition by using a default argument.funcs = []
for i in range(3):
# The default argument captures i's value at definition time
funcs.append(lambda num=i: print(num))
for f in funcs:
f()
This trick works because default arguments are evaluated when the function is defined, not when it's called. This will correctly print:
0
1
2
This is a common interview question that tests your understanding of closures, scope, and late binding in Python.
βββββββββββββββ
By: @DataScienceQ β¨
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How does Django handle an HTTP request?
Answer:
After that, the template forms an HTML response based on the provided data, and Django sends it back to the client.
This is how Django organizes work following the MVT pattern: URL β view β logic and data β template β HTTP response.
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How does the module import mechanism work in Python and what is
sys.path?Answer:
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Interview question
What will this code output and why?
Try to answer yourself first, then check the explanation.
Step-by-step:
Variable a refers to the list [1, 2, 3].
b = a
Now b refers to the same list as a.
Not a copy, but the very same object in memory.
a += [4, 5]
Important: for lists, += works as an in-place modification:
this is roughly the same as:
a.extend([4, 5])
That is, elements 4 and 5 are added to the existing list that both a and b refer to.
Therefore:
Both point to the same modified list.
Part 2. Tuples
a = (1, 2, 3)
b = a
a += (4, 5)
print(a)
print(b)
Tuples are immutable, and here the magic begins.
a = (1, 2, 3)
a refers to the tuple (1, 2, 3).
b = a
b refers to the same tuple (1, 2, 3).
a += (4, 5)
For tuples, += cannot modify the object in place (they are immutable).
So Python does this:
a = a + (4, 5)
That is, a new tuple (1, 2, 3, 4, 5) is created and variable a is reassigned to it.
b still points to the old tuple (1, 2, 3).
Therefore:
print(a) # (1, 2, 3, 4, 5)
print(b) # (1, 2, 3)
The trick
For the list, a += [4, 5] mutates the object in place, visible through all variables referencing it.
For the tuple, a += (4, 5) creates a new object and reassigns only a.
In summary:
If you want, I can explain another tricky question β about mutable default arguments in functions or about loops and closures.
https://t.iss.one/DataScienceQ
What will this code output and why?
a = [1, 2, 3]
b = a
a += [4, 5]
print(a)
print(b)
a = (1, 2, 3)
b = a
a += (4, 5)
print(a)
print(b)
Try to answer yourself first, then check the explanation.
Part 1. Lists
a = [1, 2, 3]
b = a
a += [4, 5]
print(a)
print(b)
Step-by-step:
a = [1, 2, 3]
Variable a refers to the list [1, 2, 3].
b = a
Now b refers to the same list as a.
Not a copy, but the very same object in memory.
a += [4, 5]
Important: for lists, += works as an in-place modification:
this is roughly the same as:
a.extend([4, 5])
That is, elements 4 and 5 are added to the existing list that both a and b refer to.
Therefore:
print(a) # [1, 2, 3, 4, 5]
print(b) # [1, 2, 3, 4, 5]
Both point to the same modified list.
Part 2. Tuples
a = (1, 2, 3)
b = a
a += (4, 5)
print(a)
print(b)
Tuples are immutable, and here the magic begins.
a = (1, 2, 3)
a refers to the tuple (1, 2, 3).
b = a
b refers to the same tuple (1, 2, 3).
a += (4, 5)
For tuples, += cannot modify the object in place (they are immutable).
So Python does this:
a = a + (4, 5)
That is, a new tuple (1, 2, 3, 4, 5) is created and variable a is reassigned to it.
b still points to the old tuple (1, 2, 3).
Therefore:
print(a) # (1, 2, 3, 4, 5)
print(b) # (1, 2, 3)
The trick
For the list, a += [4, 5] mutates the object in place, visible through all variables referencing it.
For the tuple, a += (4, 5) creates a new object and reassigns only a.
In summary:
# List part:
[1, 2, 3, 4, 5]
[1, 2, 3, 4, 5]
# Tuple part:
(1, 2, 3, 4, 5)
(1, 2, 3)
If you want, I can explain another tricky question β about mutable default arguments in functions or about loops and closures.
https://t.iss.one/DataScienceQ
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What are optimistic and pessimistic locking in the context of databases?
Answer:
Pessimistic locking assumes conflicts are likely. Therefore, data is locked immediately upon reading or writing and remains locked until the end of the transaction. This prevents concurrent modifications but reduces scalability and can lead to deadlocks.
Optimistic locking assumes conflicts are rare. Data is read without locking, and before committing changes, a version check is performed to see if someone else has modified the data. If so, the transaction is rolled back and retried. This approach offers better performance under low contention.
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What is ORM and what is SQLAlchemy used for?
Answer:
With SQLAlchemy, you can describe tables as classes, rows as objects, and perform SELECT, INSERT, UPDATE, DELETE operations through Python methods.
This simplifies working with databases, makes the code more readable, reduces the risk of SQL injections, and facilitates maintenance and migrations.
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What is the difference between
pass, continue, and break?Answer:
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How does the
map() function work?Answer:
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