Learn Python Coding
Open in Telegram
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. Admin: @HusseinSheikho || @Hussein_Sheikho
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Network:📈 Analytical overview of Telegram channel Learn Python Coding
Channel Learn Python Coding (@pythonre) in the English language segment is an active participant. Currently, the community unites 40 348 subscribers, ranking 3 227 in the Technologies & Applications category and 9 434 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 40 348 subscribers.
According to the latest data from 14 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 244 over the last 30 days and by 19 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.04%. Within the first 24 hours after publication, content typically collects 1.21% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 226 views. Within the first day, a publication typically gains 488 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as math, harvard, oxford, supervision, waybienad.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“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.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Thanks to the high frequency of updates (latest data received on 15 September, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
40 348
Subscribers
+1924 hours
+1427 days
+24430 days
Data loading in progress...
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| Date | Subscriber Growth | Mentions | Channels | |
| 15 September | 0 | |||
| 14 September | +21 | |||
| 13 September | +57 | |||
| 12 September | +32 | |||
| 11 September | +10 | |||
| 10 September | +12 | |||
| 09 September | +13 | |||
| 08 September | +12 | |||
| 07 September | +19 | |||
| 06 September | +7 | |||
| 05 September | +22 | |||
| 04 September | +23 | |||
| 03 September | +14 | |||
| 02 September | +17 | |||
| 01 September | +13 |
Channel Posts
Repost from Machine Learning with Python
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| 5 | Creating Nested Dictionary Values using `setdefault()` 🔥
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.
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| 18 | Why identical arguments can create different entries in `lru_cache`? 🤔
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)
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