Python Functions for Data Analytics

🔹 Python Practice – Working with Dictionaries & Data Handling 🔹 Today I practiced Python dictionaries and explored how to work with key-value data effectively 🐍 Here’s what I worked on: ✔️ Accessing values using keys ✔️ Performing arithmetic operations with type conversion ✔️ String indexing within dictionary values 💡 Sample snippet: bdict={'a':'10','b':'40','c':'50','d':'praveen','e':'fun','f':'joy'} print(bdict['b']) print(bdict['d']) print(int(bdict['b']) + int(bdict['c'])) print(bdict['d'][4]) 📌 Key takeaway: Understanding how to manipulate dictionary data and convert types is essential for real-world tasks like data processing, scripting, and automation. 🚀 Learning step by step and building strong Python fundamentals! #Python #Learning #Programming #DevOps #Automation #CodingJourney

Stop treating Python variables like boxes. They are actually labels. Understanding this architectural shift is the difference between writing robust code and chasing "ghost" bugs for hours. Here is how to master your data architecture in Python: Know your materials: Immutable types (like int, str, and tuple) are like stone—they cannot be changed in place. Mutable types (like list and dict) are like clay—they can be reshaped without creating a new object. The "Hashability" Price: Only immutable objects are hashable, meaning they can serve as dictionary keys or set elements. If you try to use a mutable list as a key, Python will throw a TypeError. The Default Argument Trap: Never use a mutable type (like []) as a function's default argument. These are created only once at definition, meaning every call to that function will share and modify the same list. Copy with Caution: When dealing with nested structures, a "shallow copy" only creates a new outer container while sharing the inner objects. Always be explicit and use copy.deepcopy() if you need a completely independent version. The Golden Rule: Use is None for identity checks rather than == None to ensure faster, more reliable null handling. Are you building with stone or clay today? #Python #DataArchitecture #CodingTips #SoftwareEngineering

🚀 ✨ 𝐃𝐀𝐘 10: 𝐔𝐍𝐃𝐄𝐑𝐒𝐓𝐀𝐍𝐃𝐈𝐍𝐆 𝐃𝐈𝐂𝐓𝐈𝐎𝐍𝐀𝐑𝐈𝐄𝐒 ✨ Today, I explored another powerful data structure in Python — 💻 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐢𝐞𝐬. 🔹 📘 𝐖𝐡𝐚𝐭 𝐀𝐫𝐞 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐢𝐞𝐬? Dictionaries store data in 𝐤𝐞𝐲-𝐯𝐚𝐥𝐮𝐞 𝐩𝐚𝐢𝐫𝐬, making it easy to organize and access information. 🔹 ⚙️ 𝐖𝐡𝐚𝐭 𝐈 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 ✔️ Creating and accessing 𝐝𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐢𝐞𝐬 ✔️ Using 𝐤𝐞𝐲𝐬(), 𝐯𝐚𝐥𝐮𝐞𝐬(), 𝐢𝐭𝐞𝐦𝐬() ✔️ Adding, updating, and deleting data 🔹 🧠 𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 Dictionaries are widely used for 𝐟𝐚𝐬𝐭 𝐝𝐚𝐭𝐚 𝐥𝐨𝐨𝐤𝐮𝐩 and handling structured data. 💡 𝐑𝐢𝐠𝐡𝐭 𝐝𝐚𝐭𝐚 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 = 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐜𝐨𝐝𝐞! 💪 𝐅𝐞𝐞𝐥𝐢𝐧𝐠 𝐦𝐨𝐫𝐞 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐭 with handling complex data! 🚀 𝐊𝐞𝐞𝐩 𝐩𝐮𝐬𝐡𝐢𝐧𝐠 𝐟𝐨𝐫𝐰𝐚𝐫𝐝! #Python #Day10 #CodingJourney #Dictionaries #DataStructures #LearningPython #Consistency 🚀

🧠 Ever felt like Python is hiding secrets inside your data? The truth is… it is you just need to know how to access them think of your data like a book 📖 Every word, every letter has a position. that’s exactly what indexing does in Python it lets you pinpoint any item inside: strings lists tuples --- Want the first letter of your name? name = "Adeel" print(name[0]) Output: A --- But it gets more powerful… Slicing = reading a part of the story print(name[0:3]) Output: Ade --- 🔍 Searching inside data? "dee" in name 👉 Output: True 📍 Finding exact position? name.index("e") The mindset shift: You’re not just writing code… You’re navigating data like a pro From picking single values → to extracting patterns → to analyzing real datasets Most beginners skip this… But this is where real understanding begins. #Python #DataAnalytics #Coding #LearnPython #Programming #TechSkills #DataScience #Beginners #100DaysOfCode

Episode 9: What I Can Do With Python One common challenge in data cleaning is this: How do you quickly see all the unique values across every column in a dataset? Not just one column at a time…but the entire dataset in one view. If you’ve worked with real data, you know how important this is. It helps you spot inconsistencies, compare entries with a data dictionary, and decide what needs to be cleaned or standardised. This week, I attempted to do exactly that. My first instinct was Excel. I tried combining functions, nesting formulas, and exploring different approaches to get all unique entries across columns at once. It sounded like something that should be possible, but after spending quite some time on it, I couldn’t get exactly what I wanted. And VBA wasn’t something I wanted to rely on (you would know about this from my previous post 😩). So I switched to Python. I wrote a simple function (maybe not so simple) and brought it back into Excel using Python. I called it 'uniq_row_per_col()' . The function takes a dataset (as an Excel range) and returns the unique values for each column. It assumes the first row contains headers, handles duplicate column names automatically (similar to pandas), and keeps case sensitivity so inconsistencies can be clearly identified. In practice, this makes data cleaning much easier. Instead of checking columns one by one, I can now: — View all unique entries across the dataset at once — Compare them directly with a data dictionary Identify inconsistencies quickly (typos, casing differences, variations) — Decide what needs standardisation or removal Behind the scenes, pandas handles the data manipulation, while xlwings manages the interaction between Excel and Python. This is something I’m beginning to appreciate more, not just using tools as they are, but extending them to fit the workflow I need. I’ve attached a short demo video showing how it works. Would this be useful in your data cleaning process? See you in Episode 10 🚀 #WhatICanDoWithPython #Python #DataCleaning #Excel #DataAnalysis #Automation #BuildInPublic #xlwings

🚀 Day 3 of My Data Science Journey — Python Fundamentals: I/O, Formatting & Variables After setting up my complete development environment on Day 2, today’s focus was on diving deeper into Python fundamentals and understanding how things work behind the scenes. 𝐓𝐨𝐩𝐢𝐜𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝: – Input & Output — working with input() and print() functions – Output Formatting — using sep= and end= for better control – String Formatting — f-strings and .format() for clean, readable output – Type Conversion — converting data types using int(), str(), etc. – Number Base Systems — working with binary, octal, and hexadecimal values – Error Handling — understanding common issues like TypeError – Variable Naming — writing clean, readable, and professional code Today’s key learning: Python’s simplicity and powerful built-in functions make coding more efficient and readable. Small concepts like formatting and proper variable naming can significantly improve code quality. Excited to keep building and strengthening my fundamentals step by step. 📊 Read the full breakdown with examples on Medium 👇 https://lnkd.in/dHveMPTz #DataScienceJourney #Python #Learning #Programming #Developers #CareerGrowth

🐍 Don't Confuse Yourself — Learn Python the Right Way! Most people start Python and get lost within weeks. Not because Python is hard — but because they don't know what to learn and in what order. Here's a structured roadmap to go from beginner to proficient: 🔹 Python Basics Syntax → Data Types → Variables → Operators → Control Flow → Functions → Modules 🔹 Object-Oriented Programming Classes & Objects → Inheritance → Polymorphism → Encapsulation → Abstraction 🔹 Data Structures Lists | Tuples | Dictionaries | Sets 🔹 File Handling Text & Binary Files | CSV | JSON 🔹 Pandas — Data Analysis Powerhouse DataFrames → Filtering → Merging → GroupBy → Pivot Tables → Data Cleaning → Advanced topics with Dask & Scikit-learn 🔹 NumPy — The Foundation of Scientific Python Arrays → Slicing → Reshaping → Broadcasting → Fourier Transforms → Vectorization → Performance Optimization Master these, and you're not just "learning Python" — you're thinking in Python. 📌 Save this post. Share it with someone starting their Python journey. 💬 Which section do you find most challenging? Drop it in the comments! #Python #PythonProgramming #LearnPython #DataScience #Pandas #NumPy #Programming #CodingTips #TechSkills #DataAnalysis #SoftwareEngineering #PythonDeveloper #MachineLearning #DataEngineering #LinkedInLearning

🚀 Day 11/60 – Sets in Python (Only Unique Values 🔥) What if you want no duplicates in your data? That’s where sets come in 👇 🧠 What is a Set? A set is a collection of unique values. numbers = {1, 2, 3, 3, 4} print(numbers) 👉 Output: {1, 2, 3, 4} Duplicates are automatically removed ✅ ➕ Add Items numbers.add(5) ❌ Remove Items numbers.remove(2) 🔁 Loop Through Set for num in numbers: print(num) ⚡ Real Example (Remove Duplicates from List) nums = [1, 2, 2, 3, 4, 4] unique_nums = set(nums) print(unique_nums) ❌ Common Mistake numbers = {} # ❌ This is a dictionary Correct: numbers = set() # ✅ Empty set 🔥 Pro Tip Sets are: ✅ Fast ✅ Unordered ✅ No duplicates 🔥 Challenge for today 👉 Create a list with duplicate values 👉 Convert it into a set 👉 Print unique values Comment “DONE” when finished ✅ Follow Adeel Sajjad to stay consistent for 60 days 🚀 #Python #LearnPython #PythonProgramming #Coding #Programming

Visualizing Data Structures: Demystifying Linked Lists in Python! 🐍 Reading through code for foundational data structures can sometimes feel like trying to untangle a bowl of spaghetti. Tracking pointer updates, handling out-of-bounds errors, and managing the head node—it's a lot to keep in your working memory all at once! To make this easier, I put together a comprehensive visual guide that maps out standard Linked List operations in Python, translating the raw code into a clear, step-by-step flowchart. Here is what the diagram breaks down: 🏗️ The Architecture: A look at the Node and LinkedList class structures. ➕ Insertion Logic: The exact pointer shifts required to seamlessly add nodes at the beginning vs. the middle of the list. ➖ Deletion Logic: How to safely bypass and remove nodes without breaking the entire chain. 🔄 Execution Flow: A step-by-step trace of a real Python script, watching the list state change in real-time. Abstract programming concepts become significantly easier to grasp when we can literally see the connections. Whether you are prepping for technical interviews or brushing up on fundamentals, I hope this helps make the logic click! How do you prefer to tackle learning algorithms and data structures? Are you a visual learner, or do you prefer to dive straight into writing the code? Let me know below! 👇 #Python #DataStructures #Coding #VisualLearning #TechCareers

Many beginners write Python code like this when working with datasets: Loop through each row Perform calculations one by one. Example: for i in range(len(df)): df.loc[i, "total"] = df.loc[i, "price"] * df.loc[i, "quantity"] It works. But it’s slow, especially with large datasets. Thats why most data scientists avoid Python loops for data operations. Instead, they use vectorized operations with libraries like Pandas and NumPy. Example: df["total"] = df["price"] * df["quantity"] Same result. Much faster. Why? Vectorized operations process entire columns at once, using optimized low-level code under the hood. That means: ⚡ Faster computations ⚡ Cleaner code ⚡ Better performance on large datasets In many cases, vectorization can make Python 10–100× faster. Simple rule: ❌ Avoid loops for large data operations ✅ Use vectorized operations whenever possible Small change. Huge performance difference. Follow for simple explanations of Data Science and Python concepts. #python #datascience #machinelearning #pandas