Pandas len() works due to dunder method in DataFrame class

Python Libraries One library can save you 5 hours. The wrong one can cost you 5 days. That is the real Python skill no one teaches. You do not need to master every Python library. You need to know exactly which one solves the problem in front of you. Here are the top Python libraries every data professional should know in 2026 👇 ✅ NumPy ↳ Fast numerical computations, array and matrix operations, base for scientific computing. ✅ Pandas ↳ Data cleaning, transformation, handling CSV/Excel/SQL, analysis with DataFrames. ✅ Matplotlib ↳ Basic data visualisation, static charts (line, bar), quick exploratory plots. ✅ SciPy ↳ Scientific computations, statistical functions, optimisation tasks. ✅ Scikit-learn ↳ Machine learning models, classification and regression, clustering and preprocessing. ✅ TensorFlow ↳ Deep learning models, production-scale deployment, neural network training. ✅ PyTorch ↳ Flexible deep learning, research and experimentation, dynamic model building. ✅ PySpark ↳ Big data processing, distributed computing, handling large datasets. ✅ Jupyter Notebook ↳ Interactive coding, data exploration, visualisation + notes in one place. ✅ SQLAlchemy ↳ Database ORM, query using Python, multi-database support. ✅ FastAPI ↳ High-performance APIs, ML model deployment, async support. ✅ Flask ↳ Lightweight web apps, simple API creation, quick model serving. ✅ Plotly ↳ Interactive charts, dashboards, real-time visualisation. ✅ Selenium ↳ Browser automation, scraping dynamic sites, UI testing. ✅ BeautifulSoup ↳ Web scraping basics, HTML parsing, extracting structured data. Here is the truth, you do not become a better data professional by learning more libraries. You become better by knowing when to reach for each one. Save this. Revisit it the next time you are stuck picking the right tool. Which library do you use most? 👇 ♻️ Repost to help another data pro sharpen their Python toolkit. 🔔 Follow for more ♻️ I share cloud , data analysis/data engineering tips, real world project breakdowns, and interview insights through my free newsletter. #python #developer #softwaredevelopment

Python Libraries One library can save you 5 hours. The wrong one can cost you 5 days.  That is the real Python skill no one teaches. You do not need to master every Python library. You need to know exactly which one solves the problem in front of you. Here are the top Python libraries every data professional should know in 2026 👇 ✅ NumPy ↳ Fast numerical computations, array and matrix operations, base for scientific computing. ✅ Pandas ↳ Data cleaning, transformation, handling CSV/Excel/SQL, analysis with DataFrames. ✅ Matplotlib ↳ Basic data visualisation, static charts (line, bar), quick exploratory plots. ✅ SciPy ↳ Scientific computations, statistical functions, optimisation tasks. ✅ Scikit-learn ↳ Machine learning models, classification and regression, clustering and preprocessing. ✅ TensorFlow ↳ Deep learning models, production-scale deployment, neural network training. ✅ PyTorch ↳ Flexible deep learning, research and experimentation, dynamic model building. ✅ PySpark ↳ Big data processing, distributed computing, handling large datasets. ✅ Jupyter Notebook ↳ Interactive coding, data exploration, visualisation + notes in one place. ✅ SQLAlchemy ↳ Database ORM, query using Python, multi-database support. ✅ FastAPI ↳ High-performance APIs, ML model deployment, async support. ✅ Flask ↳ Lightweight web apps, simple API creation, quick model serving. ✅ Plotly ↳ Interactive charts, dashboards, real-time visualisation. ✅ Selenium ↳ Browser automation, scraping dynamic sites, UI testing. ✅ BeautifulSoup ↳ Web scraping basics, HTML parsing, extracting structured data. Here is the truth, you do not become a better data professional by learning more libraries. You become better by knowing when to reach for each one. Save this. Revisit it the next time you are stuck picking the right tool. Which library do you use most? 👇 ♻️ Repost to help another data pro sharpen their Python toolkit. 🔔 Follow Abhisek Sahu for more ♻️ I share cloud , data analysis/data engineering tips, real world project breakdowns, and interview insights through my free newsletter. 🤝 Subscribe for free here → https://lnkd.in/ebGPbru9 #python #developer #softwaredevelopment

python libraries for data professionals this will be helpful for techies I got a clear idea of this library.

Core Python libraries powering data and backend systems: NumPy | Pandas | Matplotlib | SciPy Scikit-learn | TensorFlow | PyTorch FastAPI | Flask | SQLAlchemy From data processing to building APIs and real-world applications. 🚀 #Python #DataScience #Backend #Developers

Stop drowning in Python tutorials. 🛑 Most people fail Data Science not because they lack content, but because they lack order. Here is the 7-step roadmap to mastery (Start learning withe the DS roadmap https://lnkd.in/gKDjNVkg): 1️⃣ Python Fundamentals (The "Practical" Only) Don’t learn everything. Just the essentials: Variables & Data Types Loops & Logic Functions File Handling 2️⃣ NumPy (Performance Layer) The backbone of ML. Master: Vectorized operations Array manipulation Slicing & Indexing 3️⃣ Pandas (The Workhorse) 🐎 90% of your job is here. Focus on: DataFrames & Series Handling missing values Groupby, Merge, & Pivot tables 4️⃣ Visualization (The Storytelling) Insights are useless if you can't show them: Matplotlib (The basics) Seaborn (Statistical plots) 5️⃣ EDA (The Data Scientist Mindset) Start asking "Why": Summary statistics Correlations & Outliers Distribution patterns 6️⃣ Real-World Data (Beyond Notebooks) Connect to the real world: SQL + Python (Crucial!) APIs & Web Scraping Small-scale Data Pipelines 7️⃣ Build & Ship (The Portfolio) Stop "learning," start "building": Sales trends dashboard Customer churn analysis Automated data cleaning scripts The Shortcut? There isn't one. Just the right sequence. [https://prachub.com/] Why most people fail? They jump to Step 7 before mastering Step 3. Or they get stuck in "Tutorial Hell" at Step 1. My Advice: Learn 20% of the syntax. Build 80% of the time. Which step are you currently on? Let’s discuss in the comments! 👇

Two parent classes. Same method name. One child class. Which one does Python call? I assumed Python would just crash — or at least throw an error. It didn't. It silently picked one. And I had no idea which one or why. ━━━━━━━━━━━━━━━━━━━━━━ This is Multiple Inheritance in Python. class A: ····def hello(self): print("Hello from A") class B: ····def hello(self): print("Hello from B") class C(A, B): ····pass C().hello() ━━━━━━━━━━━━━━━━━━━━━━ Output: Hello from A But why A and not B? Python follows something called MRO — Method Resolution Order. It uses an algorithm called C3 Linearization. The rule is simple: Python reads left to right in the inheritance list, then goes up. C → A → B → object So it finds hello() in A first — and stops there. ━━━━━━━━━━━━━━━━━━━━━━ You can actually see Python's MRO yourself: print(C.mro) Output: ▶ C → A → B → object ━━━━━━━━━━━━━━━━━━━━━━ My Software Engineering brain connected this immediately. In Java, multiple inheritance isn't even allowed for classes — exactly because of this ambiguity. Java forces you to use interfaces instead. Python allows it — but quietly follows a strict order behind the scenes. The lesson: Python is never random. There's always a rule. You just have to find it. ━━━━━━━━━━━━━━━━━━━━━━ Senior developers — has MRO ever caused a bug in your production code that took you a while to trace? Genuinely curious how often this actually bites people. #Python #OOP #DataScience #SoftwareEngineering

Let me tell you something that the bootcamp industry does not want you to hear. Python is not data science. And before you close the post, hear me out. I work in model validation. I review data science models for a living. And I promise you, I have seen beautifully written code produce completely useless results. Clean syntax, wrong conclusions. Nobody caught it because everyone in the room was impressed by the notebook. Most LLMs can write decent Python now. For free. In 10 seconds. So if anyone can code, what actually makes you valuable as a data scientist? Well, here's my take: 1. Statistical intuition. Knowing when your model is lying to you even when it runs perfectly. 2. Business domain. Knowing which problem is actually worth solving in the first place. 3. And communication. Because if the person making the decision doesn't understand your output, your model does not exist. Python gets you in the door. These three things keep you in the room. So no, being good at Python doesn't make you a good data scientist. It makes you a good programmer. And there's a big difference.

I ran a synthetic control earlier. My result was a corner solution. I went "Hmmm. Something ain't right👀👀👀👀👀👀👀 I thought about it for a few moments, and then I was like "Oh okay no wonder, this makes total sense because of this factor, this factor and this one." Problem solved within 5 minutes. you being able to code the Fibonacci sequence or solve a LeetCode will do you very little good. Python, SQL, these are but merely the tools we use to put our thoughts into practice. and once you've gotten past the tools and to the "into practice" part, that it what an LLM cannot do for you.

🚀 Lecture 2 is Done! — Teaching Python to Think & Decide                                 Lecture 2 of my Introduction to Python course for MSBA students at SZABIST  Islamabad just wrapped up, and this is where things get really interesting!       In Lecture 1, we gave Python instructions. In Lecture 2, we taught Python how   to make decisions.  Here's what we covered:                                 🔹 Boolean Values                                 Every decision in programming boils down to: True or False? Python's Boolean  data type has just these two values. Every financial model that says "if      revenue exceeds threshold, then…" is powered by this idea.                                            🔹 Comparison Operators  We learned to let Python compare values:  ✅ Equal (==), Not equal (!=)  ✅ Greater than (>), Less than (<), and their or-equal variants          Key lesson: = assigns a value, == compares two values. Mixing these up is the   most common beginner mistake!                                                                     🔹 Boolean Operators                                Real decisions combine multiple conditions — "Approve the loan if credit score   > 700 and debt-to-income < 40%."                         📌 and — True when both conditions are True  📌 or — True when at least one is True                       📌 not — Flips True to False and vice versa  🔹 Blocks & Indentation                              Python uses indentation to define code blocks — forcing clean, readable code  from day one. The colon (:) signals that an indented block is coming next.                🔹 if / elif / else — The Core of Flow Control                   ✅ if — runs code only when a condition is True  ✅ elif — checks another condition when the previous was False  ✅ else — catches everything else                                            Python evaluates top to bottom and executes only the first match. Order      matters — just like structuring decision logic in risk models.                                                     🔹 From Flowcharts to Code  Flow control maps directly onto flowcharts. Diamonds are conditions,  rectangles are code blocks. If you can draw a decision flowchart, you can     write it in Python.                                            💡 Key Takeaway:  Flow control powers every automated decision in finance — credit scoring,  fraud detection, portfolio rebalancing. Python's if statement does what      Excel's IF() does, but with far more power and scalability.                                            📚 Resource: https://lnkd.in/dWM25WeX  Stay tuned — Lecture 3 is coming up next! 🐍  #Python #MSBA #BusinessAnalytics #Finance #PythonForFinance #Teaching #SZABIST   #DataScience #LearningPython #FlowControl