DLeX: AI Python
هوشمصنوعی و برنامهنویسی توییتر : https://twitter.com/NaviDDariya لینک پیام ناشناس، جهت انتقادات، حرف و حدیث و پیشنهادات : https://t.me/mahtabak_nashenas_bot?start=1dOIcE18ITmb5Htw1M7zgq
Ko'proq ko'rsatish📈 Telegram kanali DLeX: AI Python analitikasi
DLeX: AI Python (@ai_python) Forsiy til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 21 284 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 6 234-o'rinni va Eron mintaqasida 16 500-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 21 284 obunachiga ega bo‘ldi.
13 Noyabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -58 ga, so‘nggi 24 soatda esa -9 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 8.24% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.67% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 1 754 marta ko‘riladi; birinchi sutkada odatda 569 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 18 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent مصنوعی, توییتر, ماهواره, داده, فناوری kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“هوشمصنوعی و برنامهنویسی
توییتر :
https://twitter.com/NaviDDariya
لینک پیام ناشناس، جهت انتقادات، حرف و حدیث و پیشنهادات : https://t.me/mahtabak_nashenas_bot?start=1dOIcE18ITmb5Htw1M7zgq”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 14 Noyabr, 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.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import tkinter as TK
fig = plt.figure(figsize=(7, 7))
ax = fig.add_axes([0, 0, 1, 1], frameon=False)
ax.set_xlim(0, 1), ax.set_xticks([])
ax.set_ylim(0, 1), ax.set_yticks([])
n_drops = 50
rain_drops = np.zeros(n_drops, dtype=[('position', float, 2),('size', float, 1),('growth',float1),('color', float4)])
rain_drops['position'] = np.random.uniform(0, 1, (n_drops, 2))
rain_drops['growth'] = np.random.uniform(50, 200, n_drops)
scat = ax.scatter(rain_drops['position'][:, 0], rain_drops['position'][:, 1],
s=rain_drops['size'], lw=0.5, edgecolors=rain_drops['color'],
facecolors='none')
def update(frame_number):
# Get an index which we can use to re-spawn the oldest raindrop.
current_index = frame_number % n_drops
# Make all colors more transparent as time progresses.
rain_drops['color'][:, 3] -= 1.0/len(rain_drops)
rain_drops['color'][:, 3] = np.clip(rain_drops['color'][:, 3], 0, 1)
# Make all circles bigger.
rain_drops['size'] += rain_drops['growth']
# Pick a new position for oldest rain drop, resetting its size,
# color and growth factor.
rain_drops['position'][current_index] = np.random.uniform(0, 1, 2)
rain_drops['size'][current_index] = 5
rain_drops['color'][current_index] = (0, 0, 0, 1)
rain_drops['growth'][current_index] = np.random.uniform(50, 200)
# Update the scatter collection, with the new colors, sizes and positions.
scat.set_edgecolors(rain_drops['color'])
scat.set_sizes(rain_drops['size'])
scat.set_offsets(rain_drops['position'])
animation = FuncAnimation(fig, update, interval=10)
plt.show()
#پایتون #آموزش #کلاس_آموزشی #آموزش_کلاسی #فیلم #python
❇️ @AI_Python
✴️ @AI_Python_EN
🗣 @AI_Python_Arxivwordstring = ' ' ' jomle morede nazaretoon ro benevisid! ' ' '
wordlist = wordstring.split()
wordfreq = [wordlist.count(w) for w in wordlist]
print("String\n {} \n".format(wordstring))
print("List\n {} \n".format(str(wordlist)))
print("Frequencies\n {} \n".format(str(wordfreq)))
print("Pairs\n {}".format(str(dict(zip(wordlist, wordfreq)))))
#پایتون #آموزش #کلاس_آموزشی #آموزش_کلاسی #فیلم #python
❇️ @AI_Python
✴️ @AI_Python_EN
🗣 @AI_Python_Arxivimport random
article = ["the", "a", "one", "some", "any"]
noun = ["boy", "girl", "dog", "town", "car"]
verb = ["drove","jumped","ran","walked", "skipped"]
preposition = ["to","from", "over", "under", "on"]
def random_int():
return random.randint(0,4)
def random_sentence():
"""Creates random and return sentences."""
return ("{} {} {} {} {} {}" .format(article[random_int()] ,noun[random_int()] ,verb[random_int()] ,preposition[random_int()]
, article[random_int()]
,noun[random_int()])).capitalize()
# prints random sentences
for sentence in list(map(lambda x: random_sentence(), range(0, 20))):
print(sentence)
print("\n")
story = (". ").join(list(map(lambda x: random_sentence(), range(0, 20))))
# prints random sentences story
print("{}".format(story))
#پایتون #آموزش #فیلم #python
❇️ @AI_Python
✴️ @AI_Python_EN
🗣 @AI_Python_Arxiv# Writing to an excel
# sheet using Python
import xlwt
from xlwt import Workbook
# Workbook is created
wb = Workbook()
# add_sheet is used to create sheet.
sheet1 = wb.add_sheet('Sheet 1')
sheet1.write(1, 0, 'ISBT DEHRADUN')
sheet1.write(2, 0, 'SHASTRADHARA')
sheet1.write(3, 0, 'CLEMEN TOWN')
sheet1.write(4, 0, 'RAJPUR ROAD')
sheet1.write(5, 0, 'CLOCK TOWER')
sheet1.write(0, 1, 'ISBT DEHRADUN')
sheet1.write(0, 2, 'SHASTRADHARA')
sheet1.write(0, 3, 'CLEMEN TOWN')
sheet1.write(0, 4, 'RAJPUR ROAD')
sheet1.write(0, 5, 'CLOCK TOWER')
wb.save('xlwt example.xls')
#پایتون #آموزش #فیلم #python
❇️ @AI_Python
✴️ @AI_Python_EN
🗣 @AI_Python_Arxiv