Shivendu kumar Mandal’s Post

Part 4: Python Programming in One Page --> pandas (Python for Data) pandas are backbone of datascience. https://lnkd.in/gK8n-GjQ This is Part 4 of the One Page Learning Series. Next: matplotlib in one page Follow Scooplist for more #python #programming #pandas #datascience

Working with messy data in Excel lately, and I came across a really useful add-in that makes data abstraction much easier. What stood out for me is the ability to write Python directly inside Excel — no need to switch to VS Code or any separate environment. It simplifies the workflow and makes handling complex data tasks much more efficient. Combining Excel with Python is a powerful way to level up data analysis and streamline the entire process. Definitely worth exploring if you deal with real-world messy data. 🚀 #Leaning #Python #Datacleaning #DataExtraction #Excelwithpython

5 Pandas functions I use almost every day. If you come from SQL, these will feel familiar right away. 1. query() Filter rows the same way you would use a WHERE clause. 2. groupby() Aggregate your data by category. The Python equivalent of GROUP BY. 3. merge() Combine two DataFrames together. Works just like a JOIN. 4. value_counts() Count how often each value appears in a column. Great for a quick data quality check. 5. fillna() Replace missing values with a default. One line instead of a whole if-else block. The full code is in the image. Which one do you use the most? #Python #Pandas #DataScience #SQL #LearningInPublic

Day 3 — Data Structures in Python Today I learned: • Lists • Tuples • Sets • Dictionaries Practiced these concepts with real-world examples to understand how data is stored and managed Key takeaway: Data structures make it easier to organize, access, and manage data efficiently. Example: {"name": "Rahul", "marks": 85} Small step, but feels powerful already. GitHub: https://lnkd.in/gNxJa4TR #Python #DataStructures #CodingJourney #LearningInPublic #Consistency

Organizing Data Lists & Tuples Day 5: If variables are containers, today I learned how to build a warehouse. I dove deep into Lists and Tuples. Lists: Perfect for items that need to change, grow, or be sorted (Mutable). Tuples: The "set it and forget it" version (Immutable). Learning how to slice through a list felt like a superpower. It’s amazing how much more organized your logic becomes when you know how to group your data properly. A Python tuple is an ordered and immutable collection of elements enclosed in parentheses, commonly used for grouping related data that should not change. #DataStructures #PythonLists #LearningInPublic #TechJourney #Pythonday5

🆃🆈🅿🅴 🅲🅰🆂🆃🅸🅽🅶 🪄🐍 📦 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗧𝘆𝗽𝗲 𝗖𝗮𝘀𝘁𝗶𝗻𝗴? Definition: Type Casting is the process of converting the value of one data type (integer, string, float, etc.) to another data type. Sometimes we have data in one format, but we need it in another to actually use it or perform math on it. In Python, we might have the number "5". It looks like a number, but because it’s in quotes, it’s a String (text). We can't multiply it until we "freeze" it into an Integer! 📝 𝐓𝐡𝐞 𝐓𝐰𝐨 𝐖𝐚𝐲𝐬 𝐭𝐨 𝐂𝐚𝐬𝐭 𝐈𝐦𝐩𝐥𝐢𝐜𝐢𝐭 𝐂𝐚𝐬𝐭𝐢𝐧𝐠: If we add an integer (5) and a float (2.5), Python automatically turns the result into a float (7.5) so we don't lose any data. 𝗘𝘅𝗽𝗹𝗶𝗰𝗶𝘁 𝗖𝗮𝘀𝘁𝗶𝗻𝗴:This is when we tell Python to change the type using built-in functions like int(), float(), or str(). #python #typecasting #datacleaning #dataanalytics #learningpython #pythonsimplified

Data cleaning shouldn't be a headache. 🐍💻 Most of a Data Analyst's time isn't spent building models—it’s spent cleaning the mess. I’ve put together a minimalist Data Cleaning in Python Cheat sheet covering the essential steps to get your datasets "analysis-ready" in minutes. What’s inside: ✅ Standardizing formats & strings ✅ Handling duplicates & missing values ✅ Filtering outliers with the IQR method ✅ Quick data exploration commands Whether you're using Pandas for the first time or just need a quick syntax refresher, keep this one bookmarked.  #DataScience #DataAnalytics #Python #Pandas #DataCleaning #CodingTips #MachineLearning

🐍 Working with data? Save this. Honest truth — I keep coming back to these commands more than I'd like to admit. In most data projects, cleaning takes up more time than the actual analysis, and having the right commands at hand makes a real difference. This Python Data Cleaning cheat sheet covers the 5 essentials I rely on constantly: ✅ Handling nulls and duplicates ✅ Quickly inspecting your dataset ✅ Renaming, converting & cleaning columns ✅ Filtering and slicing rows efficiently ✅ Merging and grouping data If you work with pandas regularly, this should always be within reach. Which of these do you use the most? 👇 #Python #DataScience #DataCleaning #Pandas #DataAnalytics

📊 Pandas Explained (Data Handling Library) Pandas is used to analyze and manipulate data. 🔹 Key Functions: ✔ read_csv() → Load dataset ✔ head() → View first rows ✔ tail() → View last rows ✔ info() → Dataset info ✔ describe() → Summary stats ✔ drop() → Remove columns ✔ fillna() → Handle missing data 💡 Pandas makes data cleaning super easy! #Pandas #DataAnalysis #Python

If you are doing data analysis in Python, pandas pivot tables are one of the most powerful tools you can master. They let you go from raw, messy data to a clean, structured summary in just a few lines of code —grouping by multiple dimensions, applying aggregation functions, handling missing values, and adding totals automatically. Once you understand pivot tables, your data analysis workflow becomes significantly faster and more insightful. If you are still doing everything manually with loops and conditional logic, it is time to learn pivot tables. Read the full post here: https://lnkd.in/eCaBFSB5 #Python #Pandas #DataScience #DataAnalysis #DataEngineering #Analytics