Дальнейшие публикации и коды - будут и в MAX. Группа - Автоматизация рутинных задач
Signed Александр К

Channel
@ds_r_and_python
On this record: Topic · Growth · Engagement · Posts · Citations · Handles named that no longer answer · Cite this entry
16subscribers
+1 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001622179565 |
|---|---|
| Type | Channel |
| Username | @ds_r_and_python |
| Created | Between 1 December 2021 and 30 April 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 12 August 2026 |
| Last confirmed live | 9 October 2026 |
| Measurements held | 3 |
| Confirmed unchanged | 2 times, most recently 9 October 2026 |
| On Telegram | t.me/ds_r_and_python |
Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 1 October 2026 and assigned it the closest of 31 fixed categories, at 99% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 29 Sept 2026, 07:54 | 16 | +1 |
| 12 Aug 2026, 00:01 | 15 | no change |
| 7 Aug 2026, 20:58 | 15 | first reading |
20 posts held, back to 24 September 2023 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 5 March 2026. An engagement rate over an empty window would be a number about nothing.
Дальнейшие публикации и коды - будут и в MAX. Группа - Автоматизация рутинных задач
Signed Александр К
https://max.ru/join/lGqgHKTXf0L3d05SkEOh4F2N83HdaqH_6BsDbLCYU8A
Signed Александр К
Photo, posted without a caption
Signed Александр К
Пример использования цепей Маркова для генерации текста import os import pandas as pd import markovify from tqdm import tqdm # Папка с текстами folder_path = "data" # Чтение текстовых файлов в один DataFrame files = [f for f in os.listdir(folder_path) if f.endswith(".txt")] # Создаем список для хранения данных data = [] # Чтение каждого файла и добавление в DataFrame for file in tqdm(files, desc="Чтение файлов")…
Signed Александр К
Задача: узнать методом Монте-Карло, какое в среднем будет расстояние между двумя случайными точками в круге import numpy as np import matplotlib.pyplot as plt # Метод Монте Карло # Количество точек для симуляции num_points = 10000 # Генерация случайных точек в квадрате [0, 1] x [0, 1] x = np.random.uniform(0, 1, num_points) y = np.random.uniform(0, 1, num_points) # Определение, попадает ли точка в круг (радиус = 1…
Signed Александр К
Поиск имен собственных с использованием pymorphy3 import pandas as pd from sklearn.feature_extraction.text import CountVectorizer from sklearn.metrics.pairwise import cosine_similarity import nltk from nltk.corpus import stopwords import pymorphy3 from wordcloud import WordCloud import matplotlib.pyplot as plt # Загрузка стоп-слов для русского языка nltk.download('stopwords') russian_stopwords = stopwords.words('ru…
Signed Александр К
Пример кода на Python с использованием spaCy для выделения имен собственных. import spacy # Загрузка многоязычной модели для spaCy nlp = spacy.load("xx_ent_wiki_sm") # Текст для анализа text = "Иван и Марья Ивановна были в парке. Петр и Ирина пошли в магазин." # Применяем spaCy для анализа текста doc = nlp(text) # Выделяем имена собственные (на основе модели NER) persons = [] for ent in doc.ents: if ent.lab…
Signed Александр К
Подготовка данных и проведение факторного анализа. Шаг 1: Генерация данных Реальные данные могут быть представлены в различных формах: текст, изображения, аудио, видео и т.д. На начальном этапе данные могут находиться в форме текста, изображений, аудиофайлов и т.п., но для анализа они должны быть приведены к численному формату. В R это делается с помощью функций, которые считывают данные из файлов или преобразуют и…
Signed Александр К
Channel name was changed to «Изучаем анализ данных на языках R и Python»
Изучаем анализ данных на языках R и Python pinned a file
Channel name was changed to «NLP в Data Science. Стилометрия и борьба с плагиатом.»
Кульбак-Лейбнер (Kullback-Leibler Divergence): используется для измерения различия между двумя распределениями вероятностей, что может быть полезно в задачах сравнения текстов. Мера Жаккара (Jaccard Similarity): используется для сравнения сходства между множествами (например, набором слов в текстах). Косинусное сходство: часто используется для измерения сходства между векторными представлениями текстов.
Signed Александр К
Showing the 12 most recent of 20 posts we hold for @ds_r_and_python. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Named by 1 registered channel — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
@ds_r_and_python named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
References a handle that is not a live channel — we have no record it ever was one.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 29 September 2026 — this entry's latest reading, not the date you are reading this.
“Изучаем анализ данных на языках R и Python” (@ds_r_and_python), 16 subscribers as measured 29 September 2026. Telegram Register, tgregister.com/channel/ds_r_and_python.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.