Sambou D.’s Post

📊 Python Pandas | Selecting Data from a Series Learning how to access data efficiently is a key step in mastering Pandas. In this example, I practiced selecting values from a Pandas Series using: 🔹 .loc[] → label-based indexing 🔹 .iloc[] → position-based indexing Understanding the difference between these two helps write clearer and more reliable data analysis code. Small steps, consistent practice, and steady progress 🚀 #Python #Pandas #DataAnalysis #DataScience #LearningPython #CodingJourney #PythonProgramming

Most ML bugs don’t crash your code. They quietly change your data. Views vs copies in Pandas, silent mutations, and data leakage can ruin results without warnings. Good models don’t start with algorithms. They start with trustworthy data. #DataScience #MachineLearning #Python #Pandas #CleanData #MLWorkflow #SoftwareEngineer

🚀 #Python f-Strings — The Cleanest Way to Format Output If you're still using "Hello " + name in Python, it's time to level up. f-strings make your code cleaner, faster, and far more readable. With f-strings, you can directly embed variables inside curly braces { }, making them perfect for dashboards, logs, data engineering, and real-world apps. Here’s a simple example using an employee attendance snippet: employee_name = "John Doe" attendance_status = "Present" check_in_time = "09:15 AM" print(f"Employee: {employee_name}, Status: {attendance_status}, Check-in: {check_in_time}") “There is one error in the attached image—let’s see if you can spot it.” 😄 #python #learning #practise #coding

“Understanding Python Dictionaries changed the way I handle real-world data.” Lists store values. Tuples protect values. Dictionaries give meaning to values. From APIs to ML features, dictionaries are everywhere. If you know dicts well, Python becomes powerful 🚀 📌 Today’s whiteboard: DICTIONARY in Python 🔖 Hashtags #Python #DataStructures #DSA #PythonLearning #DataScience #MachineLearning #FastAPI #CodingJourney #LearnWithMe #Learn_with_Bharath

🚀 Python Tip: Use set for membership checks — it’s not optional, it’s fundamental Understanding data structures and how they work under the hood is one of the simplest ways to improve performance. Why is set faster for membership checks? set is implemented as a hash table → average O(1) lookup list is a dynamic array → O(n) linear scan Rule of thumb: * If you care about existence, use a set. * If you care about order or duplicates, use a list. (Because set contains unique elements) Small choices like this make a big difference in production code. #Python #CleanCode #SoftwareEngineering #ProgrammingTips #BestPractices

Had messy,duplicated records in a dataset today. Used Python Pandas to clean it up: • drop_duplicates() • fillna() • Converted date columns Data cleaning is 70% of the job,but honestly,I enjoy it. Clean data → Better insights. #Python #Pandas #DataCleaning

Day 38 / 100 – Implement Stack Using Python List => Building a basic stack data structure with core operations: push, pop, top, and empty. Time Complexity: O(1) for all operations => Python lists make stack operations efficient: append() for push pop() for removing the top element Index access for top Length check for empty state Learning Insight This problem show how choosing the right data structure makes implementation simple and efficient. Python lists are dynamic arrays, making them a perfect fit for stack behavior when operations are restricted to one end. Consistency and clarity matter more than complexity — mastering the basics builds a strong DSA foundation. rewrite in simple word so not look loke ai Code pushed to Git https://lnkd.in/g3NUT5HM #100DaysOfCode #Python #DSA #Stack #DataStructures #LeetCode #CodingJourney #ProblemSolving

🐍 Python Essentials: The Building Blocks 🚀 Mastering the fundamentals is the secret to scaling your code. Here’s a 60-second refresher on Python basics: 🔹 Data Types: Python handles Integers (whole), Floats (decimal), and Booleans (logic). Use Typecasting to switch between them seamlessly. 🔹 Expressions: Beyond basic math, use // for integer division to keep your results as whole numbers. 🔹 Strings are Immutable: You can’t change a string once it’s created—operations like .upper() or .replace() actually generate a new string. 🔹 Indexing & Slicing: Access any character using positive or negative indices, or use strides to skip through a sequence. 🔹 Variable Logic: Variables store data, but remember: re-assigning a variable overrides the previous value. Foundation is everything. Whether you're building AI models or automating workflows, these basics stay the same. What was the hardest Python concept for you to wrap your head around when you started? #Python #Programming #CodingTips #DataScience #SoftwareEngineering

The Latency Manifesto: Uncomfortable Truth 8 of 8 Axiom 8: Do Not Include Python in a Sentence Containing "Low Latency" This will upset many but it is the truth. Python is a phenomenal language. It's fantastic for: - Prototyping - Data analysis - Machine learning pipelines - Configuration and orchestration - Anything where developer velocity matters more than execution speed It is not for your hot path. The GIL exists. The interpreter overhead exists. Function call overhead exists. "But we use NumPy/Cython/PyPy—" So then you're not really using Python in the hot path, are you? You've acknowledged the problem and worked around it. The rule: Python is excellent for deciding what to do. The doing happens in something else.