Learn Python Coding
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Learn Python through simple, practical examples and real coding ideas. Clear explanations, useful snippets, and hands-on learning for anyone starting or improving their programming skills.

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Function

pprint()
pprint()

is designed for pretty-printed, formatted output in Python. 🐍

It automatically formats complex data structures, such as nested lists, dictionaries, and tuples, by adding indentation and line breaks for improved readability. ✨

Features:
β€’ Supports customization of the output width for convenient formatting.
β€’ Includes depth and compact parameters to control the level of nesting and display compactness.
β€’ Available through the standard pprint module in the Python library. πŸ’»

#Python #Programming #Code #Developer #Tech #Scripting

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httptap

This is a command-line tool that breaks down an HTTP request into individual stages: DNS resolution, TCP connection, TLS handshake, and data transfer.

It generates a detailed waterfall timeline, which allows you to quickly identify which stage of the request is causing the delay.

πŸ“ Language: #Python 99%

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More: https://github.com/ozeranskii/httptap
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Python Tip 🐍

In Python, you can unpack both a string and a list using the same code. ✨

# Example usage
text = "abc"
lst = [1, 2, 3]
# Unpacking works similarly for both
a, b, c = text
x, y, z = lst

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Why identical arguments can create different entries in `lru_cache`? πŸ€”

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)


πŸ”₯ A consistent call format prevents unnecessary cache misses and redundant execution of expensive operations.

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Creating Nested Dictionary Values using `setdefault()` πŸ”₯

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.

πŸ”₯ 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.

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