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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

Network:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert India6 735 Technologies & Applications2 585...

📈 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.

📊 Audience metrics and dynamics

Since its creation on невідомо, 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.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Find 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

3 072

Post views

~ 49824 hours

No data48 hours

5.96%

Engagement rate

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Posts per day

Ads index

beta

Posts Archive

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9. Example Projects - Part 02

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9. Example Projects - Part 01

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8. Data Visualization

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7. Working with Data Part 3

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6. Working with Data Part 2

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5. Working with Data Part 1

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4. Intro to Pandas

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3. Learning Numpy

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2. Setup

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1. Intro to Course and Python

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🔰 Learning Python for Data Analysis and Visualization ⏱ 21 Hours 📦 110 Lessons Learn python and how to use it to analyze,visualize and present data. Includes tons of sample code and hours of video! Taught By: Jose Portilla Download Full Course: https://t.me/pythonanalyst/26 Download All Courses: https://t.me/DataAnalystInterview

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Python Generators In Python, a generator is a function that returns an iterator that produces a sequence of values when iterated over. Generators are useful when we want to produce a large sequence of values, but we don't want to store all of them in memory at once. There is a lot of complexity in creating iteration in Python; we need to implement iter() and next() method to keep track of internal states. It is a lengthy process to create iterators. That's why the generator plays an essential role in simplifying this process. If there is no value found in iteration, it raises StopIteration exception.

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Output of this code? x = lambda a, b, c : a + b + c print(x(5, 6, 2))

Anonymous voting

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What will be the output of this code in python? x = lambda a, b : a * b print(x(5, 6))

Anonymous voting

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Example: Add 10 to argument a, and return the result:

x = lambda a : a + 10
print(x(5))

Output will be 15

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Python Lambda: A lambda function is a small anonymous function. A lambda function can take any number of arguments, but can only have one expression. lambda arguments : expression The expression is executed and the result is returned. Use lambda functions when an anonymous function is required for a short period of time.

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Syntax: newlist = [expression for item in iterable if condition == True] The return value is a new list, leaving the old list unchanged. The condition is like a filter that only accepts the items that valuate to True. The condition is optional and can be omitted. The iterable can be any iterable object, like a list, tuple, set etc. The expression is the current item in the iteration, but it is also the outcome, which you can manipulate before it ends up like a list item in the new list.

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👉 List comprehensions: List comprehension offers a shorter syntax when you want to create a new list based on the values of an existing list. Example: Based on a list of fruits, you want a new list, containing only the fruits with the letter "a" in the name. Without list comprehension you will have to write a for statement with a conditional test inside: fruits = ["apple", "banana", "cherry", "kiwi", "mango"] newlist = [] for x in fruits: if "a" in x: newlist.append(x) print(newlist) With list comprehension you can do all that with only one line of code:

fruits = ["apple", "banana", "cherry", "kiwi", "mango"]

newlist = [x for x in fruits if "a" in x]

print(newlist)

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Data Visualization in Python with Dash.zip137.29 MB

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Python Cheat_Sheet -cheatography.pdf0.44 KB