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Python for Data Analysts

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

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πŸ“ˆ Analytical overview of Telegram channel Python for Data Analysts

Channel Python for Data Analysts (@pythonanalyst) in the English language segment is an active participant. Currently, the community unites 51 569 subscribers, ranking 2 585 in the Technologies & Applications category and 6 735 in the India region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 51 569 subscribers.

According to the latest data from 30 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -222 over the last 30 days and by -13 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 5.96%. Within the first 24 hours after publication, content typically collects 0.97% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 0 views. Within the first day, a publication typically gains 498 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
  • Thematic interests: Content is focused on key topics such as visualization, panda, analyst, sql, analytic.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œFind top Python resources from global universities, cool projects, and learning materials for data analytics. For promotions: @coderfun Useful links: heylink.me/DataAnalytics”

Thanks to the high frequency of updates (latest data received on 31 October, 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.

51 569
Subscribers
-1324 hours
-707 days
-22230 days
Posts Archive
5 High-growth career paths in Tech: Tech careers in 2024 Discover the trends, skills, and opportunities that will define tech careers in 2024. πŸ“† Date: December 28th, 2023 πŸ•’ Time:  8PM-10PM Free Registration Link: πŸ‘‡πŸ‘‡ https://bit.ly/47clbV6 Don't miss out on this opportunity to elevate your Data Science career!  ENJOY LEARNING πŸ‘πŸ‘

Python Cheat Sheet.pdf17.29 MB

Head First Python Paul Barry, 2023

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Python for Data Science Roadmap πŸ‘‡πŸ‘‡ https://t.me/DataScienceInterviews/6

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

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Natural Language Processing - 2024 #python #en

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)

PythonCrashCourse.pdf5.38 MB

150 SQL Queries for Practice πŸ‘‡πŸ‘‡ https://t.me/DataAnalystInterview/170

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Data Visualization with Python Dr. Pooja, 2023

Python Data Cleaning Cookbook Michael Walker, 2023

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

Learning Professional Pythonβœ… https://www.linkedin.com/posts/sql-analysts_learn-python-activity-7129340888232714240-ZuMM Like for more πŸ‘β€οΈ

Python for Everybody Charles R. Severance, 2023

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Profound Python Libraries Onder Teker, 2022