Python Lambda Functions & Data Transformation

🧹 Essential Python Commands for Data Cleaning Data cleaning takes up to 70–80% of a data professional’s time. Mastering the right Pandas commands can save hours and make your workflow far more efficient. Here’s a quick cheat sheet covering: ✅ Data inspection (df.head(), df.info(), df.describe()) ✅ Handling missing values (isnull(), dropna(), fillna()) ✅ Cleaning & transformation (drop_duplicates(), rename(), astype(), replace()) ✅ Filtering & selection (loc[], iloc[]) ✅ Aggregation & analysis (groupby(), value_counts(), pivot_table()) ✅ Merging & combining datasets (concat(), merge(), join()) #Python #DataScience #DataAnalytics #MachineLearning #Pandas #Programming #DataCleaning #100DaysOfCode #LifeLongLearning #ContinuousLearning

🚀 30 𝐃𝐚𝐲𝐬 𝐨𝐟 𝐏𝐲𝐭𝐡𝐨𝐧 — 𝐃𝐚𝐲 #03 | 𝐃𝐚𝐭𝐚 𝐓𝐲𝐩𝐞𝐬 & 𝐓𝐲𝐩𝐞 𝐂𝐚𝐬𝐭𝐢𝐧𝐠 Day 3 focused on one of the most fundamental concepts in programming: Data Types and Type Conversion in Python. Understanding data types is critical because every operation in Python depends on how data is stored and interpreted. 📌 𝘒𝘦𝘺 𝘊𝘰𝘯𝘤𝘦𝘱𝘵𝘴 𝘐 𝘊𝘰𝘷𝘦𝘳𝘦𝘥: 🔹 Core Data Types in Python int → Integer values float → Decimal values str → String/Text values bool → Boolean (True/False) 🔹 Type Checking Used the built-in type() function to inspect variable data types and better understand how Python handles memory and operations. 🔹 Type Conversion (Type Casting) Learned explicit type conversion using: int() float() str() bool() 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭: Converting "20" (string) into 20 (integer) allows mathematical operations. Without proper type casting, programs can throw errors or behave unexpectedly. 💡 𝘛𝘦𝘤𝘩𝘯𝘪𝘤𝘢𝘭 𝘛𝘢𝘬𝘦𝘢𝘸𝘢𝘺: Data types directly impact arithmetic operations, memory handling, and program logic. Mastering type casting reduces bugs and improves code reliability. Strong fundamentals lead to scalable skills. 𝑫𝒂𝒚 3 𝒄𝒐𝒎𝒑𝒍𝒆𝒕𝒆 — 𝒄𝒐𝒏𝒔𝒊𝒔𝒕𝒆𝒏𝒄𝒚 𝒄𝒐𝒏𝒕𝒊𝒏𝒖𝒆𝒔. ✅ #PythonProgramming #PythonBasics #DataTypes #TypeCasting #TypeConversion #LearnToCode #CodingJourney #30DayChallenge #SoftwareDevelopment #WomenInTech #TechSkills #ProgrammingLife #ContinuousLearning

Watch me clean Real Life dataset that was full of inconsistencies and wrong values. Normally you don't find a dataset like this one on internet. I used Python and little bit of excel to clean the data and identify the wrong values. So its also kind of an audit that i did. If you are learning data analysis or data science, watching the full workflow can help you understand how real datasets are handled. 📂 GitHub Repository All the files used in this video (dataset + Python code) will be available on GitHub. 🐙 GitHub: ➡️ https://lnkd.in/dXvxusJ6 🛠 Tools Used 🐍 Python 🐼 Pandas 📊 Microsoft Excel 📚 What You Will Learn Understanding a raw dataset Identifying messy and inconsistent data Cleaning data using Python Pandas Fixing formatting issues Handling missing values Preparing data for analysis 👍 If You Found This Helpful Like the video Subscribe for more Data Science / Data Analysis content Check the GitHub repository for the full code #DataCleaning #Python #Pandas #DataAnalysis #DataScience #PythonForDataAnalysis #Excel #RealWorldDataset #DataCleaningProject https://lnkd.in/dbEeRedN

🔥 Day 67 — Python vs SQL “Which One Should You Learn First?” 🐍 Python General-purpose programming language Best for automation, ML, AI, scripting Can analyze, clean & visualize data Flexible for both backend & data tasks 🗄️ SQL Specially for databases Used to store, manipulate & retrieve data Foundation of every data-related job Fast & powerful for managing large datasets ⭐ Quick Verdict Learn SQL first → if you want to work with data Learn Python first → if you want automation, ML, or development Best combo → Python + SQL = Data Superpower #Python #SQL #PythonVsSQL #DataAnalytics #DataEngineer #ProgrammingLife #DevelopersOfLinkedIn #TechLearning #100DaysOfCode #CodingJourney #BackendDevelopment #DatabaseManagement #LearnToCode #KaifTechTalks

🚀 Day 8/70 – Functions in Python Today I learned about Functions in Python 🐍 A function is a reusable block of code that performs a specific task. In Data Analytics, functions help us: ✔ Avoid repeating code ✔ Organize logic clearly ✔ Build reusable analysis steps ✔ Improve code readability 📌 Basic Function Syntax def greet(): print("Hello, Data World!") greet() 📌 Function with Parameters def add_numbers(a, b): return a + b result = add_numbers(10, 5) print(result) 👉 Output: 15 📊 Data Analytics Example def calculate_average(marks): total = sum(marks) return total / len(marks) marks = [70, 80, 90, 60] average = calculate_average(marks) print("Average:", average) Using functions makes analysis clean, structured, and reusable 🔥 💡 Why Functions Matter in Real Projects? ✔ Modular coding ✔ Easier debugging ✔ Better scalability ✔ Essential for automation & data pipelines Consistency builds confidence 💪 8 Days Done. Improving every single day. #Day8 #Python #DataAnalytics #LearningInPublic #FutureDataAnalyst #70DaysChallenge

SQL teaches you how to think about data. Python teaches you how to work with data. Together they help you: ✔ automate reports ✔ analyze large datasets ✔ build predictive models ✔ visualize trends In the data world, SQL + Python is a power combo. #DataScience #SQL

Understanding decision-making in Python is a key step toward building strong analytical logic. I worked with conditional statements(if, elif, else) to control program flow based on different conditions. These conditions. These concepts are essential for handling real-world data scenarios, applying business rules, and making data-driven decisions through code. For Data Analysts and Business Analysts, decision-making logic helps in filtering data, automating processes, and deriving meaningful insights efficiently. Strengthening these fundamentals is an important step in my analytics journey. #Python #DataAnalysis #BusinessAnalysis

Day 46 of my Data Engineering journey 🚀 Today I learned about scheduling and automation with Python an important step toward building real data pipelines. 📘 What I learned today (Automation in Python): • Why automation is essential in data engineering • Running scripts automatically instead of manually • Using Python’s schedule library • Understanding cron jobs for scheduled tasks • Automating repetitive data workflows • Building scripts that run daily or hourly • Thinking about reliability in automated jobs • Moving from scripts → pipelines In real data systems, data pipelines run automatically. No one manually runs scripts every day. Automation is what turns code into a real data pipeline. Why I’m learning in public: • To stay consistent • To build accountability • To improve daily Day 46 done ✅ Next up: connecting Python with databases 💪 #DataEngineering #Python #Automation #LearningInPublic #BigData #CareerGrowth #Consistency

Day 14 – File Handling in Python Today I learned how to work with files in Python....an essential skill for real-world data analysis. Until now, we were working with manually created data inside scripts. Today, I learned how to read and write data from external files. What I learned: • Opening files using open() • Reading file content • Writing data into files • Using different file modes (r, w, a) • Using with statement for safe file handling Why This Matters in Data Analytics: In real-world scenarios: •Data comes from CSV files •Reports are saved as text files •Logs are generated automatically •Output needs to be stored externally •File handling is the first step toward working with real datasets before moving into Pandas. Data analysis starts when you can read real data. GitHub Repository: https://lnkd.in/gdD4yAvR #Python #DataAnalytics #LearningInPublic #DataAnalystJourney #ProgrammingBasics #CareerGrowth

Python is great for data science. But using it to clean data is overkill. A popular YouTube tutorial shows how to clean SurveyMonkey data using Python and Pandas, it took the developer 1 hour. The same transformation in Power Query? 5 minutes. Most data analysts don't realize Excel can do this. They assume Python is the only serious option for data cleaning. But Power Query has been built into Excel since 2010, and it handles transformations like unpivoting, merging, grouping, and calculated columns without writing a single line of code. In this video, I walk through the exact same dataset and show you how to clean it 12x faster using Power Query. If you've been putting off learning Python just to clean data, you don't need to. Watch the video and download the practice file: https://lnkd.in/d7E3TiDU ❓Do you use Python or Power Query for data cleaning? #Excel #Python #DataCleaning