Python for Data Analysts
Open in Telegram
Find top Python resources from global universities, cool projects, and learning materials for data analytics. For promotions: @coderfun Useful links: heylink.me/DataAnalytics
Show more
Network:📈 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 51 575 subscribers, ranking 2 592 in the Technologies & Applications category and 6 741 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 51 575 subscribers.
According to the latest data from 29 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -220 over the last 30 days and by -8 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 5.83%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
- Post reach: On average, each post receives 0 views. Within the first day, a publication typically gains 0 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 30 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.
51 575
Subscribers
-824 hours
-737 days
-22030 days
Posts Archive
51 570
Python: 2024 Data Analytics Mastery!
✅ Python Basics: Start with syntax, variables, and basic operations.
✅ Data Structures: Get a grip on lists, dictionaries, sets, and tuples.
✅ Control Structures: Master if-else, loops, and exception handling for logic flow.
✅ Functions and Modules: Learn to write reusable code pieces.
✅ Dive into Pandas: Learn DataFrame and Series, data importing/exporting, and basic data operations.
✅ Data Wrangling with Pandas: Master data cleaning, transformation, and aggregation techniques.
✅ Advanced Pandas: Explore time series, categorical data, and efficient data manipulation.
✅ NumPy Introduction: Understand NumPy arrays, array indexing, and array math.
✅ Advanced NumPy: Delve into broadcasting, vectorization, and advanced array operations.
✅ Data Visualization: Create compelling visualizations with libraries like Matplotlib and Seaborn
Python Interview Resources: https://topmate.io/analyst/907371
Like for more ❤️
51 570
Python Basic Interview Questions for Freshers
[Part -2]
6) What are the tools that help to find bugs or perform static analysis?
PyChecker is a static analysis tool that detects the bugs in Python source code and
warns about the style and complexity of the bug. Pylint is another tool that verifies
whether the module meets the coding standard.
7) What are Python decorators?
A Python decorator is a specific change that we make in Python syntax to alter
functions easily.
8) What is the difference between list and tuple?
The difference between list and tuple is that list is mutable while tuple is not. Tuple
can be hashed for e.g as a key for dictionaries.
9) How are arguments passed by value or by reference?
Everything in Python is an object and all variables hold references to the objects. The
references values are according to the functions; as a result you cannot change the
value of the references. However, you can change the objects if it is mutable.
10) What is Dict and List comprehensions are?
They are syntax constructions to ease the creation of a Dictionary or List based on
existing iterable.
11) What are the built-in type does python provides?
There are mutable and Immutable types of Pythons built in types Mutable built-in
types
• List
• Sets
• Dictionaries
Immutable built-in types
• Strings
• Tuples
• Numbers
Python Interview Resources: https://topmate.io/analyst/907371
Like for more ❤️
51 570
Easy Python scenarios for everyday data tasks
Scenario 1: Data Cleaning
Question:
You have a DataFrame containing product prices with columns Product and Price. Some of the prices are stored as strings with a dollar sign, like $10. Write a Python function to convert the prices to float.
Answer:
import pandas as pd
data = {
'Product': ['A', 'B', 'C', 'D'],
'Price': ['$10', '$20', '$30', '$40']
}
df = pd.DataFrame(data)
def clean_prices(df):
df['Price'] = df['Price'].str.replace('$', '').astype(float)
return df
cleaned_df = clean_prices(df)
print(cleaned_df)
Scenario 2: Basic Aggregation
Question:
You have a DataFrame containing sales data with columns Region and Sales. Write a Python function to calculate the total sales for each region.
Answer:
import pandas as pd
data = {
'Region': ['North', 'South', 'East', 'West', 'North', 'South', 'East', 'West'],
'Sales': [100, 200, 150, 250, 300, 100, 200, 150]
}
df = pd.DataFrame(data)
def total_sales_per_region(df):
total_sales = df.groupby('Region')['Sales'].sum().reset_index()
return total_sales
total_sales = total_sales_per_region(df)
print(total_sales)
Scenario 3: Filtering Data
Question:
You have a DataFrame containing customer data with columns ‘CustomerID’, Name, and Age. Write a Python function to filter out customers who are younger than 18 years old.
Answer:
import pandas as pd
data = {
'CustomerID': [1, 2, 3, 4, 5],
'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
'Age': [17, 22, 15, 35, 40]
}
df = pd.DataFrame(data)
def filter_customers(df):
filtered_df = df[df['Age'] >= 18]
return filtered_df
filtered_customers = filter_customers(df)
print(filtered_customers)
51 570
Always remember this one thing in your life ❤️👇
https://youtube.com/shorts/ir-4oBR9ec0?feature=share
51 570
Most Important Python Topics for Data Analyst Interview:
#Basics of Python:
1. Data Types
2. Lists
3. Dictionaries
4. Control Structures:
- if-elif-else
- Loops
5. Functions
6. Practice basic FAQs questions, below mentioned are few examples:
- How to reverse a string in Python?
- How to find the largest/smallest number in a list?
- How to remove duplicates from a list?
- How to count the occurrences of each element in a list?
- How to check if a string is a palindrome?
#Pandas:
1. Pandas Data Structures (Series, DataFrame)
2. Creating and Manipulating DataFrames
3. Filtering and Selecting Data
4. Grouping and Aggregating Data
5. Handling Missing Values
6. Merging and Joining DataFrames
7. Adding and Removing Columns
8. Exploratory Data Analysis (EDA):
- Descriptive Statistics
- Data Visualization with Pandas (Line Plots, Bar Plots, Histograms)
- Correlation and Covariance
- Handling Duplicates
- Data Transformation
#Numpy:
1. NumPy Arrays
2. Array Operations:
- Creating Arrays
- Slicing and Indexing
- Arithmetic Operations
#Integration with Other Libraries:
1. Basic Data Visualization with Pandas (Line Plots, Bar Plots)
#Key Concepts to Revise:
1. Data Manipulation with Pandas and NumPy
2. Data Cleaning Techniques
3. File Handling (reading and writing CSV files, JSON files)
4. Handling Missing and Duplicate Values
5. Data Transformation (scaling, normalization)
6. Data Aggregation and Group Operations
7. Combining and Merging Datasets
51 570
🚀 Essential Python snippets to explore data:
1. .head() - Review top rows
2. .tail() - Review bottom rows
3. .info() - Summary of DataFrame
4. .shape - Shape of DataFrame
5. .describe() - Descriptive stats
6. .isnull().sum() - Check missing values
7. .dtypes - Data types of columns
8. .unique() - Unique values in a column
9. .nunique() - Count unique values
10. .value_counts() - Value counts in a column
11. .corr() - Correlation matrix
51 570
Free Resources for Python
Codebasics python tutorials (first 16) —
https://www.youtube.com/playlist?list=PLeo1K3hjS3uv5U-Lmlnucd7gqF-3ehIh0
Practice Python course
https://dabeaz-course.github.io/practical-python/Notes/Contents.html
Codebasics python HINDI tutorials —
https://www.youtube.com/playlist?list=PLPbgcxheSpE1DJKfdko58_AIZRIT0TjpO
Useful Python resources for beginners
https://t.me/programming_guide/8
Python 3 Book for beginners
https://t.me/pythondevelopersindia/272?single
51 570
30-Days-Of-Python
30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace.
Creator: Asabeneh
Stars ⭐️: 33.2k
Forked By: 6.7k
https://github.com/Azure/azure-sdk-for-python
51 570
Python from scratch
by University of Waterloo
0. Introduction
1. First steps
2. Built-in functions
3. Storing and using information
4. Creating functions
5. Booleans
6. Branching
7. Building better programs
8. Iteration using while
9. Storing elements in a sequence
10. Iteration using for
11. Bundling information into objects
12. Structuring data
13. Recursion
https://open.cs.uwaterloo.ca/python-from-scratch/
#python
51 570
+3
Python Complete Notion Notes with 5 Practical Projects
👇👇
https://topmate.io/analyst/871454
Kept price just Rs 29 so that everyone can afford it 😄❤️
51 570
This cheat sheet includes basic python required for data analysis excluding pandas, numpy & other libraries
51 570
Essential Python Libraries for Data Analytics 😄👇
Python Free Resources: https://t.me/pythondevelopersindia
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
