121
VOStop struggling with verbose Python 🛑
Here is the cleaner way to handle it in Python.
💡 Discover the power of one-liners.
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Get the Python for AI course + 6 projects at the link in bio. 🐍
@vornixlabs
#Age Function In Sql
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199
VOStop struggling with complex data structures 🛑
Here is the cleaner way to handle it in Python.
💡 Learn namedtuple, defaultdict, and deque for better code.
---
Get the Python for AI course + 6 projects at the link in bio. 🐍
@vornixlabs
664
HABad data doesn’t lie - Python just exposes it. 🔍
Day 8 of learning Python from scratch, documenting every step until I land a junior data engineer job.
Today I built a quality flag checker. Feed it a row of data — it tells you what’s wrong. Negative age? Flagged. Country code too long? Flagged. Simple logic, real use case.
This is literally what data pipelines do at scale.
I’m on day 8.
Follow along → Zero to Hired series 👇#learnpython #datascience #dataentry #learntocode #dataengineering2027
@hanga.codes
1.8K
AFIf you want to practice Python seriously (especially for Data Analytics / Data Science), these are the top Python practice websites. They help with coding skills, problem solving, and interview preparation.
@afterhours_rahmat
52.9K
SHBehind every strong data science project is a solid toolkit. From numerical computation to machine learning and deep learning, Python offers a powerful ecosystem that supports the entire analytics workflow.
If you work with data, you should be comfortable with libraries that handle array operations, structured data processing, visualization, statistical insights, and model development. These tools are not just for data scientists. They are essential for analysts, BI professionals, and machine learning practitioners who want to move from raw data to reliable insights.
The right combination of libraries allows you to clean data efficiently, build visual stories, engineer features, train predictive models, and deploy intelligent systems. Understanding when and why to use each one is what separates basic coding from professional data work.
Build depth, not just familiarity. Strong fundamentals in Python libraries will make your portfolio sharper and your problem-solving more structured.
[python, pythonlibraries, datascience, dataanalysis, machinelearning, deeplearning, numpy, pandas, matplotlib, seaborn, scikitlearn, tensorflow, keras, datavisualization, datacleaning, datawrangling, numericalcomputing, arrays, dataframe, statistics, predictiveanalytics, modelbuilding, neuralnetworks, ai, artificialintelligence, analytics, businessintelligence, programming, coding, datatools, dataprocessing, featureengineering, evaluationmetrics, eda, exploratorydataanalysis, dataengineering, bigdata, algorithm, supervisedlearning, unsupervisedlearning, regression, classification, clustering, timeseries, automation, pythonfordata, techskills, analyticscareer, datascientist, analyst]
#DataScience #Python #MachineLearning #DataAnalytics #DeepLearning
@she_explores_data
654
TH🚨 Python Dictionary Key Overwrite – Interview Trick Question 🚨
What’s the output of this Python code? 🤯
This is one of the most confusing and frequently asked Python interview questions related to Python dictionaries, hash values, data types, and key comparison.
⚠️ Be aware — ans is NOT {1: "a", 1.0: "b"}
If you're learning Python programming, preparing for coding interviews, or trying to master Python data structures, you MUST understand how Python handles dictionary keys, hashing, equality (==), and float vs int comparison.
Comment the correct output
#reelsinstagram #coding #python #interview
#developer TheDataSciQuest TDSQ
@thedatasciquest
2.3K
AF🐍 Python Day 3 – Data Types You Must Know
“Everything in Python has a type.” ⚡
Core Types:
• int → 10
• float → 10.5
• str → “Hello”
• bool → True / False
• list → [1,2,3]
Example:
x = 10
print(type(x))
Understanding types = fewer bugs.
CTA:
Type “DAY 3” if you’re consistent 🚀
Everyone out there, starting Python series is smart 💼
Since you already have SQL + analytics background, this will position you toward ML / Data Science roles strongly.
Next?
🐍 Day 4–6 (Loops + Conditions)
📊 Python for Data Analysts track
🤖 Python for ML roadmap
What direction do we take? 💪
And Follow for more
@afterhours_rahmat
315
VOStop struggling with duplicates 🛑
Here is the cleaner way to handle them in Python.
💡 Use sets for fast and efficient data operations.
#pythondeveloper #codingtips #pythonprogramming #softwareengineering #sets
---
Get the Python for AI course + 6 projects at the link in bio. 🐍
@vornixlabs
30.8K
ASTbh after being a Data Scientist for 6 years, I still don’t know some stuff on that 2nd list 😅
Trying to learn ALL of Python at once is so intimidating
Don’t put that pressure on yourself.
Instead only focus on these must-know concepts, and you can ignore stuff on the “Not Now” list for now.
MUST KNOW PYTHON CONCEPTS
• Basic syntax: variables, data types, loops
• Writing custom functions
• Lists, tuples, dictionaries
• List comprehensions
• String manipulation
• Reading and writing files
• Try/except error handling
• Importing and using libraries
• Pandas basics – Series vs DataFrame
• Selecting and filtering data
• Groupby and aggregations
• Merging or joining data
• Sorting and ranking data
• Handling missing values
• Basic plotting – matplotlib
• Working with dates – e.g. pd.to_datetime, .dt
NOT NOW
• Object oriented programming – classes, inheritance
• Generators and decorators
• Custom context managers
• Writing modules or packages
• Virtual environments and dependency management
• Multiprocessing or multithreading
• Async programming
• Advanced pandas tuning – eval, query
• Unit testing and CI/CD
• Custom exception classes
• Functional programming tricks – map, reduce, lambdas everywhere
• Building web APIs – Flask, FastAPI
#python #datascience #datascientist #datascienceinterview
@askdatadawn
264
COYour code isn’t slow. Your Data Structures are. 🛑🐍
Most developers treat Lists as a “one-size-fits-all” container. But when you’re working with millions of rows in AI or Data Science, a List membership test (x in list) is an O(n) disaster.
Python has to look at every single item until it finds a match.
The Fix? The Hash Table.
By using a Set, Python uses a hash function to jump directly to the memory “bucket” where the item lives.
✅ Result: Instant O(1) lookups.
✅ Speed: Up to 100,000x faster at scale.
✅ Logic: Cleaner, faster, and senior-level.
Stop coding like a junior. Start architecting for speed.
🚀 Join the Top 1% of AI Engineers:
Follow Corpnce for daily performance engineering.
#datascience #pythonprogramming #ai #codinglife #tips
@corpnce.ai
305
NO🚨 Most Python beginners break their code because they ignore this.
Not loops.
Not functions.
👉 Python Data Types.
If you don't understand how Python stores data, debugging becomes a nightmare.
Here are the 7 core built-in data types every Python developer must know:
✔ Integer → Whole numbers
✔ Float → Decimal numbers
✔ String → Text data
✔ List → Ordered & mutable collection
✔ Tuple → Immutable collection
✔ Set → Unique values only
✔ Dictionary → Key-value structure
These data types are the foundation of every Python program, from small scripts to AI systems.
📌 Save this post for later
🔁 Share with a Python learner
📌 Follow @nomidlofficial for more Python concepts
Read more info:
https://www.nomidl.com/python/what-are-the-common-built-in-data-types-in-python/
#PythonProgramming #LearnPython #CodingTips #MachineLearning #PythonDeveloper
@nomidlofficial
118
VOStop struggling with data processing 🛑
Here is the cleaner way to handle it in Python.
💡 Simplify your code with list comprehensions and filter.
#pythondeveloper #codingtips #pythonprogramming #softwareengineering #data_processing
---
Get the Python for AI course + 6 projects at the link in bio. 🐍
@vornixlabs
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