Python for Data Analysts

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Find top Python resources from global universities, cool projects, and learning materials for data analytics. For promotions: @coderfun Useful links: heylink.me/DataAnalytics

Red:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert India6 808 Tecnologías y Aplicaciones2 495...

📈 Análisis del canal de Telegram Python for Data Analysts

El canal Python for Data Analysts (@pythonanalyst) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 51 849 suscriptores, ocupando la posición 2 495 en la categoría Tecnologías y Aplicaciones y el puesto 6 808 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 51 849 suscriptores.

Según los últimos datos del 29 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 96, y en las últimas 24 horas de 10, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.18%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.96% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 2 167 visualizaciones. En el primer día suele acumular 499 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 7.
Intereses temáticos: El contenido se centra en temas clave como visualization, panda, analyst, sql, analytic.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Find top Python resources from global universities, cool projects, and learning materials for data analytics. For promotions: @coderfun Useful links: heylink.me/DataAnalytics”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 30 agosto, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.

51 849

Suscriptores

+1024 horas

-117 días

+9630 días

2 167

Visitas de la publicación

~ 49924 horas

~ 72448 horas

4.18%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

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Master Python with this Free Guide 👇👇 https://www.linkedin.com/posts/sql-analysts_master-python-with-this-free-guide-tips-activity-7142572820852641793-l0UX?utm_source=share&utm_medium=member_android

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Sent to all who liked and commented on the post 😄

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Python for Data Science Roadmap 👇👇 https://t.me/DataScienceInterviews/6

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Complete Python Course with Building 10 Real-world Applications 👇👇 https://www.linkedin.com/posts/sql-analysts_python-viral-pythonprogramming-activity-7141826568485605376-tf7V

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Natural Language Processing - 2024 #python #en

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Python Most Important Interview Questions Question 1: Calculate the average stock price for Company X over the last 6 months. Question 2: Identify the month with the highest total sales for Company Y using their monthly sales data. Question 3: Find the maximum and minimum stock price for Company Z on any given day in the last year. Question 4: Create a column in the DataFrame showing the percentage change in stock price from the previous day for Company X. Question 5: Determine the number of days when the stock price of Company Y was above its 30-day moving average. Question 6: Compare the average stock price of Companies X and Z in the first quarter of the year. #Data# ---------------------------------------------- import pandas as pd data = { 'Date': pd.date_range(start='2023-01-01', periods=180, freq='D'), 'CompanyX_StockPrice': pd.np.random.randint(50, 150, 180), 'CompanyY_Sales': pd.np.random.randint(20000, 50000, 180), 'CompanyZ_StockPrice': pd.np.random.randint(70, 200, 180) } df = pd.DataFrame(data)

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PythonCrashCourse.pdf5.38 MB

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150 SQL Queries for Practice 👇👇 https://t.me/DataAnalystInterview/170

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I'm earning real cash from DigiWards, the HIGH PAYING Reward App! You can also earn money by entering my invitation code AQCBFJ to get free 1,000 points. Download DigiWards now to earn big rewards! https://play.google.com/store/apps/details?id=com.digiwards.app

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https://www.linkedin.com/posts/sql-analysts_python-dataanalytics-activity-7135473022206889984-XUxd

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Data Visualization with Python Dr. Pooja, 2023

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Python Data Cleaning Cookbook Michael Walker, 2023

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Python Interview Questions for data analyst interview Question 1: Find the top 5 dates when the percentage change in Company A's stock price was the highest. Question 2: Calculate the annualized volatility of Company B's stock price. (Hint: Annualized volatility is the standard deviation of daily returns multiplied by the square root of the number of trading days in a year.) Question 3: Identify the longest streaks of consecutive days when the stock price of Company A was either increasing or decreasing continuously. Question 4: Create a new column that represents the cumulative returns of Company A's stock price over the year. Question 5: Calculate the 7-day rolling average of both Company A's and Company B's stock prices and find the date when the two rolling averages were closest to each other. Question 6: Create a new DataFrame that contains only the dates when Company A's stock price was above its 50-day moving average, and Company B's stock price was below its 50-day moving average

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Learning Professional Python✅ https://www.linkedin.com/posts/sql-analysts_learn-python-activity-7129340888232714240-ZuMM Like for more 👍❤️

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Python for Everybody Charles R. Severance, 2023

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Profound Python Libraries Onder Teker, 2022

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Building Python Microservices with FastAPI.pdf6.62 MB

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Which of the following is/are immutable in Python?

Anonymous voting

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Python Data Science Handbook Python Data Science Handbook: full text in Jupyter Notebooks. This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks. Creator: Jake Vanderplas Stars⭐️: 39k Fork: 17.1K Repo: https://github.com/jakevdp/PythonDataScienceHandbook For more, join https://t.me/pythonanalyst

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Python for Data Analysts - Quick Summary (1).pdf0.64 KB