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@pythonadvisorai · canal · indexada desde 2026-07-19
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Pandas Part 1.pdf · 352 KB · clique para mostrar
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Pandas Part 2.pdf · 519 KB · clique para mostrar
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🔥 Master These 30 Algorithms to Boost Your Coding Skills! 🚀 - Binary Search 🕵️‍♂️ - Quick Sort ⚡ - Merge Sort 🌊 - Heap Sort 🏰 - BFS 🌐 - DFS 🌲 - Dijkstra’s Shortest Path 🚗 - Bellman-Ford 🚦 - Floyd-Warshall 🌉 - Kruskal’s Minimum Spanning Tree 🌳 - Prim’s Algorithm 🌿 - KMP Pattern Matching 🔍 - Rabin-Karp Search 🧮 - Dynamic Programming 🧠 - Kadane’s Max Subarray Sum 💥 - Floyd’s Cycle Detection 🔄 - Topological Sort 🗂️ - Backtracking 🎯 - Binary Tree Traversals 🌳 - Segment Tree 📊 - Union-Find Disjoint Set 🔗 - Greedy Algorithms 🎯 - Bit Manipulation 💡 - Sliding Window ⏱️ - Two Pointers 🔀 - Hashing 🔑 - Recursion 🔁 - Divide & Conquer ⚔️ - Graph Coloring 🎨 - A* Search 🗺️ Master these, ace interviews, and become a problem-solving pro! 💪🔥 ***
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🚀 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮𝗻 𝗔𝗜/𝗟𝗟𝗠 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗠𝗮𝘀𝘁𝗲𝗿 𝘁𝗵𝗲 𝘀𝗸𝗶𝗹𝗹𝘀 𝘁𝗲𝗰𝗵 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝗵𝗶𝗿𝗶𝗻𝗴 𝗳𝗼𝗿: fine-tune large language models and deploy them to production at scale. 𝗕𝘂𝗶𝗹𝘁 𝗳𝗿𝗼𝗺 𝗿𝗲𝗮𝗹 𝗔𝗜 𝗷𝗼𝗯 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀. ✅ Fine-tune models with industry tools ✅ Deploy on cloud infrastructure ✅ 2 portfolio-ready projects ✅ Official certification + badge 📘 𝗟𝗲𝗮𝗿𝗻 𝗺𝗼𝗿𝗲 & 𝗲𝗻𝗿𝗼𝗹𝗹 ⬇️ https://go.readytensor.ai/cert-513-llm-engg-and-deployment
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✅Python Interview Questions with Answers 🧑‍💻👩‍💻 1️⃣ Write a function to remove outliers from a list using IQR. import numpy as np def remove_outliers(data): q1 = np.percentile(data, 25) q3 = np.percentile(data, 75) iqr = q3 - q1 lower = q1 - 1.5 * iqr upper = q3 + 1.5 * iqr return [x for x in data if lower <= x <= upper] 2️⃣ Convert a nested list to a flat list. nested = [[1, 2], [3, 4],] flat = [item for sublist in nested for item in sublist] 3️⃣ Read a CSV file and count rows with nulls. import pandas as pd df = pd.read_csv('data.csv') null_rows = df.isnull().any(axis=1).sum() print("Rows with nulls:", null_rows) 4️⃣ How do you handle missing data in pandas? ⦁ Drop missing rows: df.dropna() ⦁ Fill missing values: df.fillna(value) ⦁ Check missing data: df.isnull().sum() 5️⃣ Explain the difference between loc[] and iloc[]. ⦁ loc[]: Label-based indexing (e.g., row/column names) Example: df.loc[0, 'Name'] ⦁ iloc[]: Position-based indexing (e.g., row/column numbers) Example: df.iloc 💬 Tap ❤️ for more!
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✅Python Interview Questions with Answers 🧑‍💻👩‍💻 1️⃣ Write a function to remove outliers from a list using IQR. import numpy as np def remove_outliers(data): q1 = np.percentile(data, 25) q3 = np.percentile(data, 75) iqr = q3 - q1 lower = q1 - 1.5 * iqr upper = q3 + 1.5 * iqr return [x for x in data if lower <= x <= upper] 2️⃣ Convert a nested list to a flat list. nested = [[1, 2], [3, 4],] flat = [item for sublist in nested for item in sublist] 3️⃣ Read a CSV file and count rows with nulls. import pandas as pd df = pd.read_csv('data.csv') null_rows = df.isnull().any(axis=1).sum() print("Rows with nulls:", null_rows) 4️⃣ How do you handle missing data in pandas? ⦁ Drop missing rows: df.dropna() ⦁ Fill missing values: df.fillna(value) ⦁ Check missing data: df.isnull().sum() 5️⃣ Explain the difference between loc[] and iloc[]. ⦁ loc[]: Label-based indexing (e.g., row/column names) Example: df.loc[0, 'Name'] ⦁ iloc[]: Position-based indexing (e.g., row/column numbers) Example: df.iloc 💬 Tap ❤️ for more!
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*Free Data Analytics Webinar + Certificate by HCL GUVI: an HCL Group company.* 💻🚀 Learn Python, SQL, Excel, Power BI, & more! 📈 *Perfect for everyone* – No experience needed! ✅ Only *500 seats* left! 😳 For *Working Professionals*, Job Aspirants and *Final Year Students* 🚀 *Register now (100% Free)*👇 https://link.guvi.in/codehubb02493
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✅ *Python Scenario-Based Interview Question* 🧠🐍 You have a list: numbers = [1, 2, 3, 2, 4, 1, 5, 2] *Question:* Find the number that appears most frequently in the list. *Expected Output:* 2 *Python Code:* from collections import Counter most_common_num = Counter(numbers).most_common(1)[0][0] print(most_common_num) *Explanation:* – Counter() counts occurrences of each element – most_common(1) returns the most frequent item – Access [0][0] to get just the number 💬 *Tap ❤️ for more bite-sized Python tips!* *** Would you like the next one to be slightly more advanced (e.g., involving strings or list comprehensions)?
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💡 25 VS Code Extensions Every Dev Should Try 💻 ✅ Prettier ✅ ESLint ✅ Live Server ✅ GitLens ✅ Auto Rename Tag ✅ IntelliCode ✅ Thunder Client ✅ Material Icon Theme ✅ Bracket Pair Colorizer ✅ REST Client ✅ Tailwind CSS IntelliSense ✅ Better Comments ✅ Path Intellisense ✅ Error Lens ✅ Code Spell Checker ✅ Docker ✅ Remote SSH ✅ Tabnine ✅ Import Cost ✅ Markdown All in One ✅ Color Highlight ✅ CodeSnap ✅ TODO Highlight ✅ Inline Fold ✅ ChatGPT - CodeGPT 🔥 React “❤️” if you use VS Code daily!
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Top 50 Data Analyst Interview Questions (2025) 🎯📊 1. What does a data analyst do? 2. Difference between data analyst, data scientist, and data engineer. 3. What are the key skills every data analyst must have? 4. Explain the data analysis process. 5. What is data wrangling or data cleaning? 6. How do you handle missing values? 7. What is the difference between structured and unstructured data? 8. How do you remove duplicates in a dataset? 9. What are the most common data types in Python or SQL? 10. What is the difference between INNER JOIN and LEFT JOIN? 11. Explain the concept of normalization in databases. 12. What are measures of central tendency? 13. What is standard deviation and why is it important? 14. Difference between variance and covariance. 15. What are outliers and how do you treat them? 16. What is hypothesis testing? 17. Explain p-value in simple terms. 18. What is correlation vs. causation? 19. How do you explain insights from a dashboard to non-technical stakeholders? 20. What tools do you use for data visualization? 21. Difference between Tableau and Power BI. 22. What is a pivot table? 23. How do you build a dashboard from scratch? 49. What do you do if data contradicts business intuition? 50. What are your favorite analytics tools and why? 🎓 Data Analyst Jobs: https://whatsapp.com/channel/0029Vb6r6218kyyQgBDNjm26 💬 Tap ❤️ for the detailed answers!
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python interview question with answer.pdf · 6 KB · clique para mostrar
python interview question with answer.pdf
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🔥 The AI Trick That Got 4 Interview Calls Without Applying to Hundreds of Jobs Everyone is talking about AI. Very few people are using it to get hired. This case study shows exactly how one person used AI to stand out from thousands of applicants. 👇 Read Here“ by The AI Coder on Medium: https://medium.com/@vaishnavi.singh0324/how-one-ai-tool-helped-me-get-4-interview-calls-in-30-days-without-applying-to-hundreds-of-jobs-e362f4bb5190 Follow for more 👆
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