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@mariajose.787:

MariaJose🎀
MariaJose🎀
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Region: CO
Tuesday 06 October 2026 21:31:55 GMT
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yetsibelrincon
𝐘𝐞𝐭𝐬𝐢𝐛𝐞𝐥 𝐑𝐢𝐧𝐜ó𝐧💋 :
Como debe ser😝
2026-10-06 21:34:27
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mil05769
Cinthya San♡👑✨ :
2026-10-07 02:07:58
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davidbenitez.89
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2026-10-06 21:36:58
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Hội cờ tướng của xóm | #phamvinh99 #vulaci #vietcomedy
Hội cờ tướng của xóm | #phamvinh99 #vulaci #vietcomedy
Chiếc váy vàng chấm bi làm bé cười càng thêm rạng rỡ 💛 Dáng váy xòe xinh xắn, họa tiết chấm bi vừa nổi bật vừa đáng yêu Diện đi chơi, dạo phố hay chụp hình đều lên ảnh cực xinh 👉 Mẹ xem ngay mẫu váy cho bé trong giỏ hàng nhé! #vaybegai #dambegai #vaychambibegai #damchambibegai #vayvangbegai
Chiếc váy vàng chấm bi làm bé cười càng thêm rạng rỡ 💛 Dáng váy xòe xinh xắn, họa tiết chấm bi vừa nổi bật vừa đáng yêu Diện đi chơi, dạo phố hay chụp hình đều lên ảnh cực xinh 👉 Mẹ xem ngay mẫu váy cho bé trong giỏ hàng nhé! #vaybegai #dambegai #vaychambibegai #damchambibegai #vayvangbegai


#husky #foryou #katakata
#husky #foryou #katakata


When you want to answer questions like: 💰 Which category has the highest sales? 📊 What is the average salary by department? 🌍 Which country has the most customers? You need groupby() 🚀 🔹 WHAT DOES GROUPBY DO? Pandas groupby() follows a simple process: 🧩 SPLIT Divide the data into groups. 📊 APPLY Perform a calculation on each group. 🔗 COMBINE Combine the results into a summary. Split → Apply → Combine 🔄 📊 COMMON AGGREGATIONS Use groupby() with: ➕ sum() 📈 mean() 🔢 count() ⬆️ max() ⬇️ min() 📊 median() 🧠 EXAMPLE QUESTIONS YOU CAN ANSWER 📈 Average sales by region 💰 Total revenue by product 👥 Number of customers by city 🎓 Average score by subject 🛒 Total orders by category 🚀 ADVANCED GROUPBY You can also: 🔹 Group by multiple columns 🔹 Apply multiple aggregations 🔹 Create custom functions 🔹 Combine GroupBy with filtering 🔹 Use GroupBy with pivot tables 💡 WHY IT MATTERS groupby() turns raw rows into meaningful summaries. Instead of looking at thousands of individual records, you can quickly discover: 📊 Trends 🔍 Patterns 💰 Performance 📈 Business Insights Raw Data → GroupBy → Meaningful Insights 🚀 Save this for your Pandas and Data Science journey 📌🐼 #Pandas #Python #DataScience #creatorsearchinsights #programming
When you want to answer questions like: 💰 Which category has the highest sales? 📊 What is the average salary by department? 🌍 Which country has the most customers? You need groupby() 🚀 🔹 WHAT DOES GROUPBY DO? Pandas groupby() follows a simple process: 🧩 SPLIT Divide the data into groups. 📊 APPLY Perform a calculation on each group. 🔗 COMBINE Combine the results into a summary. Split → Apply → Combine 🔄 📊 COMMON AGGREGATIONS Use groupby() with: ➕ sum() 📈 mean() 🔢 count() ⬆️ max() ⬇️ min() 📊 median() 🧠 EXAMPLE QUESTIONS YOU CAN ANSWER 📈 Average sales by region 💰 Total revenue by product 👥 Number of customers by city 🎓 Average score by subject 🛒 Total orders by category 🚀 ADVANCED GROUPBY You can also: 🔹 Group by multiple columns 🔹 Apply multiple aggregations 🔹 Create custom functions 🔹 Combine GroupBy with filtering 🔹 Use GroupBy with pivot tables 💡 WHY IT MATTERS groupby() turns raw rows into meaningful summaries. Instead of looking at thousands of individual records, you can quickly discover: 📊 Trends 🔍 Patterns 💰 Performance 📈 Business Insights Raw Data → GroupBy → Meaningful Insights 🚀 Save this for your Pandas and Data Science journey 📌🐼 #Pandas #Python #DataScience #creatorsearchinsights #programming

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