Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
Learn Python Coding
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
Ko'proq ko'rsatish📈 Telegram kanali Learn Python Coding analitikasi
Learn Python Coding (@pythonre) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 39 140 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 511-o'rinni va Hindiston mintaqasida 10 551-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 39 140 obunachiga ega bo‘ldi.
07 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 433 ga, so‘nggi 24 soatda esa 10 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 2.62% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.01% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 1 026 marta ko‘riladi; birinchi sutkada odatda 395 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 4 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent math, harvard, oxford, supervision, waybienad kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“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”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 08 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
def eratosthenes(n):
is_prime = [True] * (n + 1)
is_prime[0] = is_prime[1] = False
for i in range(2, int(n ** 0.5) + 1):
if is_prime[i]:
for j in range(i * i, n + 1, i):
is_prime[j] = False
return is_primedef eratosthenes_odd(n):
if n < 2:
return []
size = (n + 1) // 2
is_prime = [True] * size
is_prime[0] = False
limit = int(n ** 0.5) // 2
for i in range(1, limit + 1):
if is_prime[i]:
p = 2 * i + 1
start = (p * p) // 2
for j in range(start, size, p):
is_prime[j] = False
return is_primedef eratosthenes_bytearray(n):
is_prime = bytearray(b"\x01") * (n + 1)
is_prime[0:2] = b"\x00\x00"
for i in range(2, int(n ** 0.5) + 1):
if is_prime[i]:
for j in range(i * i, n + 1, i):
is_prime[j] = 0
return is_primedef eratosthenes_fast(n):
if n < 2:
return []
size = (n + 1) // 2
is_prime = bytearray(b"\x01") * size
is_prime[0] = 0
limit = int(n ** 0.5) // 2
for i in range(1, limit + 1):
if is_prime[i]:
p = 2 * i + 1
start = (p * p) // 2
is_prime[start::p] = b"\x00" * (((size - start - 1) // p) + 1)
return is_primen = 10_000_000:
>>> eratosthenes.py
real 0.634s
>>> eratosthenes_odd.py
real 0.245s
>>> eratosthenes_bytearray.py
real 0.801s
>>> eratosthenes_fast.py
real 0.028s
Conclusions:
* skipping even numbers (#1) gives ~2.6× speedup
* bytearray itself doesn't speed up — it's more about memory
* the hybrid (#3) gives ~22.6× speedup
Key trick in #3:
is_prime[start::p] = b"\x00" * (((size - start - 1) // p) + 1)
