I’m thrilled to share that I’m proud to have designed and developed a mental health website for Saner LTD using React.js. The website is live, and you can check it out here: . Let’s connect and build something amazing together! 🌟.#webdesign #reactjs

Thank you @geegpay_hq for saving me today 😂😂😂

Join millions who have switched to Grok.

The time and consistency I put into my tech learning journey is much to an extent where someone asked me " What if this tech no later pay you", 😞😞🥺 for a while I was weak and dumbfounded. I'm still thinking about the question ❓ but still I'm not scared 🤣. #datafam

Claude by @AnthropicAI, Chatgpt @OpenAI, Copilot by @github no mistakes @_devJNS the rest know their functions 😀.

RT @_devJNS: which app do you trust to build a $10k app with no mistakes?

I earned a statement of accomplishment on DataCamp for completing Introduction to Statistics in Python!.

RT @Fenalytics: 1/.Hey #datafam . I explored a cancer dataset focused on Europe and found some powerful insights on patient outcomes, risk….

I do 😂.

RT @gozkybrain4u: Manifest for the new week under this post.

Do you remember when you joined X? I do! #MyXAnniversary

#GoodMorning.

💡 Takeaway: Understanding how to filter data effectively is a crucial skill for every aspiring data scientist, it’s the foundation of data exploration and analysis.

🔍 The beauty of pandas is that it gives us flexibility in how we access and analyze data, whether by labels or integer positions. This becomes extremely powerful when dealing with complex datasets in fields like business analytics, scientific research, and even AI model building.

2️⃣ Label-based Indexing (loc)brics.loc[:, "Area"].👉 Retrieves all rows for the Area column using labels. 3️⃣ Integer-based Indexing (iloc). brics.iloc[:, 2].👉 Retrieves all rows of the 3rd column (which is Area).

📊 For example, consider a dataset named brics with columns: Country, Capital, Area, and Population. Here are a few ways pandas allow us to filter and explore:. 1️⃣ Direct Column Access. brics['Area'].👉 This returns the Area column from the DataFrame.

In real-world datasets, we often deal with huge tables containing multiple columns and thousands (or even millions) of rows. To gain insights efficiently, we need to filter or subset our data.

✨ Good morning #LinkedIn, #DataFam, and #Datacamp Community!. Today, I’d love to share an important concept I explored in Python for Data Science - filtering data with pandas Data frames. #DataScience #Python #Pandas #LearningJourney #DataAnalytics #CareerGrowth

#MyLearningJourney.

#MyLearningJourney.

💡 Each step in Python reveals how logic and math come together to transform raw data into insight one operator at a time.

`OR` → True if at least one condition is True (`True or False → True`).`NOT` → Negates a condition (`not True → False`). Understanding these operators is essential for data filtering, conditional logic, and building efficient workflows in libraries like Pandas and NumPy.