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bewafa
bewafa
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Sunday 05 April 2026 03:43:42 GMT
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2026-09-25 10:55:12
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roomwithrealized
hari :
2026-07-17 02:57:47
1
salman.mughalaisha
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2026-04-05 15:11:32
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2026-09-29 19:01:49
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Imran khan chtta :
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2026-04-19 16:40:58
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roomwithrealized
hari :
2026-07-17 02:57:43
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2026-08-08 23:17:44
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user905542406203
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2026-04-11 13:45:52
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2026-04-12 13:14:15
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𝕬𝖑𝖎 Rajpoot :
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user2121425768292 :
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roomwithrealized
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2026-07-17 02:57:19
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2026-08-27 14:56:28
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ali.mauvia1
خانپور جو شھزادو علی معاویہ :
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2026-09-29 12:47:04
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Khalid Hussain barijo Khalid :
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2026-04-19 07:08:03
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salman.mughalaisha
Salman Mughalaisha. zoii🫀🫀🫀 :
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2026-04-05 15:11:14
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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
Ce site te permet l'utilisation de quasiment tous les modèles d'IA GRATUITEMENT ! 😉 #siteweb #astuce #ia #outils
Ce site te permet l'utilisation de quasiment tous les modèles d'IA GRATUITEMENT ! 😉 #siteweb #astuce #ia #outils

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