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Data Structures & Algorithms (Python): Practice Exams
Ace technical coding interviews with 200 questions on Big O, Graphs, Hash Maps, and Dynamic Programming.โฆ
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Data Structures & Algorithms (Python): Practice Exams
Ace technical coding interviews with 200 questions on Big O, Graphs, Hash Maps, and Dynamic Programming.โฆ
๐ Category: Development / Software Engineering
๐ฏ Level: Intermediate Level
๐ฃ Language: English
๐จโ๐ Enrolled: 317 students
โญ Rating: 0/5.0
๐ต Price:
๐ Coupon:
BC7C61AF20F4AE188D90โก Your free link unlocks automatically in a few seconds โ no ad required.
๐ By: https://t.iss.one/Udemy26
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โค2
Why identical arguments can create different entries in `lru_cache`? ๐ค
The
Although both calls pass the same value, for the cache, these are different keys, so the function will be executed twice.
The order of named arguments can also affect how an entry is created in the cache.
Therefore, it is best to call cached functions in a consistent style: either by position or by name, in the same order.
๐ฅ A consistent call format prevents unnecessary cache misses and redundant execution of expensive operations.
#Python #lru_cache #Caching #Performance #ProgrammingTips #CodeBestPractices
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The
lru_cache creates a key not only from the values of the arguments, but also from the way they are passed.load(True)
load(debug=True)
Although both calls pass the same value, for the cache, these are different keys, so the function will be executed twice.
print(load.cache_info())
# CacheInfo(hits=0, misses=2, ...)
The order of named arguments can also affect how an entry is created in the cache.
func(a=1, b=2)
func(b=2, a=1)
Therefore, it is best to call cached functions in a consistent style: either by position or by name, in the same order.
load(debug=True)
load(debug=True)
๐ฅ A consistent call format prevents unnecessary cache misses and redundant execution of expensive operations.
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Creating Nested Dictionary Values using `setdefault()` ๐ฅ
When grouping data, it's often necessary to check if a key exists, create a container for it, and then add a value.
For example, distributing users by role. Without special methods, this usually involves a separate key check.
The
The result is the same structure without a separate key existence check:
It's important to note that the expression of the second argument is evaluated every time
In this code,
๐ฅ
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When grouping data, it's often necessary to check if a key exists, create a container for it, and then add a value.
For example, distributing users by role. Without special methods, this usually involves a separate key check.
users = [
("admin", "alex"),
("user", "max"),
("admin", "kate"),
]
groups = {}
for role, name in users:
if role not in groups:
groups[role] = []
groups[role].append(name)
The
setdefault() method allows you to perform this operation directly when accessing the dictionary. If the key exists, it returns its current value. If the key is missing, the provided value is written to the dictionary and then returned:groups = {}
for role, name in users:
groups.setdefault(
role,
[],
).append(name)The result is the same structure without a separate key existence check:
print(groups)
# {
# 'admin': ['alex', 'kate'],
# 'user': ['max']
# }
It's important to note that the expression of the second argument is evaluated every time
setdefault() is called, even if the key already exists. Therefore, you should avoid creating expensive objects or performing functions with side effects there:value = cache.setdefault(
key,
build_value(),
)
In this code,
build_value() will be called before the method itself is executed. If the value creation should only happen when the key is missing, it's better to use an explicit check or a suitable data structure, such as defaultdict.setdefault() is well-suited for compactly initializing simple mutable containers when grouping and aggregating data. However, it's important to remember that the provided value is evaluated regardless of whether the key exists.#Python #Coding #Dicts #Programming #CodeTips #DevLife
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โค4
Here's a small fact about Python ๐
The
It was introduced in Python 3.8 and allows you to assign a value to a variable and use it directly within the expression at the same time.
For example:
Without it, you would have to retrieve the value separately using
Have you ever used the
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The
:= operator is called the "walrus" because the symbols resemble the eyes and tusks of a walrus ๐ฆญIt was introduced in Python 3.8 and allows you to assign a value to a variable and use it directly within the expression at the same time.
For example:
while (line := input("Say something: ")) != "quit":
print(f"You said: {line}")Without it, you would have to retrieve the value separately using
input(), and then check it.Have you ever used the
:= operator in your code?#Python #Programming #WalrusOperator #Coding #TechFacts #Python3
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Do not violate the Single Responsibility Principle ๐ฏ
A function should do one thing, and do it well.
This function does too much:
The problem here is that the calculation of the subtotal, discount, and tax are all combined into one function. Any change to one of these steps can affect the entire calculation.
It's better to break down the logic into smaller, more specialized functions:
This is much better. โ
Smaller functions with a single task are easier to test with unit tests because they have fewer dependencies and require less mocking.
Furthermore, isolated components are easier to reuse in different parts of the application or pipeline without bringing in unnecessary dependencies.
Therefore, keep your functions simple and focused.
One function โ one responsibility. ๐
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A function should do one thing, and do it well.
This function does too much:
def calculate_final_total(
price: float,
quantity: int,
discount_rate: float,
tax_rate: float
) -> float:
# Calculate the subtotal
subtotal = price * quantity
# Apply the discount
discounted_amount = subtotal * (1 - discount_rate)
# Calculate the tax
final_total = discounted_amount * (1 + tax_rate)
return final_total
The problem here is that the calculation of the subtotal, discount, and tax are all combined into one function. Any change to one of these steps can affect the entire calculation.
It's better to break down the logic into smaller, more specialized functions:
def calculate_subtotal(price: float, quantity: int) -> float:
return price * quantity
def apply_discount(subtotal: float, discount: float) -> float:
return subtotal * (1 - discount)
def calculate_tax(amount: float, tax_rate: float) -> float:
return amount * (1 + tax_rate)
This is much better. โ
Smaller functions with a single task are easier to test with unit tests because they have fewer dependencies and require less mocking.
Furthermore, isolated components are easier to reuse in different parts of the application or pipeline without bringing in unnecessary dependencies.
Therefore, keep your functions simple and focused.
One function โ one responsibility. ๐
#Python #Coding #SoftwareDevelopment #CleanCode #Programming #BestPractices
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