FastAPI Automatic Data Validation with Pydantic Models

🌐 Day 14: Data Validation with Pydantic in FastAPI ⚡ Continuing my FastAPI learning journey, today I explored how APIs handle structured data using request bodies and Pydantic models. Here’s what I worked on today: ✅ Understanding request bodies in API endpoints ✅ Creating Pydantic models to define structured data ✅ Automatic data validation and type checking ✅ Sending structured JSON data through FastAPI interactive docs (Swagger UI) ✅ Cleaner and more maintainable API code One of the most impressive aspects of FastAPI is how Pydantic models simplify validation. Instead of manually checking data, FastAPI automatically ensures the incoming data follows the defined structure. 💡 Key takeaway: Proper data validation is essential for building reliable and secure APIs. #FastAPI #Python #BackendDevelopment #APIDevelopment #DevJourney #LearningInPublic #SoftwareEngineering

Building a Patient Management API with FastAPI I recently started a project building a Patient Management API using FastAPI! This project was a deep dive into how backend systems efficiently serve and manage data. ★Learnings: # Path Parameters: Mastered how to access specific resources (like fetching a patient by a unique ID) directly through the URL structure. # Query Parameters: Implemented dynamic filtering and sorting, allowing users to drill down into records (e.g., filtering by status or department). # GET Methods: Explored the logic behind retrieving all records versus targeted data fetching. This experience gave me a much clearer picture of how data flows from the server to the client. My next goal is to harden the API with advanced validation and robust data handling using Pydantic. #FastAPI #Python #BackendDevelopment #APIs

🚀 Solved Another Sliding Window Problem on LeetCode! Today’s problem: Maximum Number of Vowels in a Substring of Given Length (LeetCode #1456) 💡 Problem Summary: Given a string s and an integer k, find the maximum number of vowels in any substring of length k. ❌ Brute Force Approach: Generate all substrings of size k Count vowels in each substring Time Complexity: O(n × k) → not efficient ✅ Optimized Approach: Sliding Window Instead of recalculating everything: Count vowels in the first window Slide the window forward: Add next character Remove previous character Track the maximum count 👉 Core Idea: count = count + new_char - old_char 💻 Clean Code: def maxVowels(s, k): vowels = set("aeiou") count = 0 for i in range(k): if s[i] in vowels: count += 1 max_count = count for i in range(k, len(s)): if s[i] in vowels: count += 1 if s[i - k] in vowels: count -= 1 max_count = max(max_count, count) return max_count ⚡ Complexity: Time: O(n) Space: O(1) 🧠 Key Takeaway: Sliding Window is not just a technique — it’s a mindset. You reuse previous computation instead of recalculating everything. 🔥 This pattern applies to: Strings & Arrays Substring / Subarray problems Real-world streaming data #DSA #LeetCode #Coding #SlidingWindow #InterviewPreparation #Python #ProblemSolving

Day 8: Cracking the Subarray Sum Logic 🧩 💡 How I solved it: Instead of a slow O(n^2) brute-force approach, I used the Prefix Sum + Hash Map technique to achieve a highly efficient O(n) solution. *Tracked the Running Sum: Maintained a current_sum as I traversed the array. *The Difference Trick: For every element, I checked if (current_sum - k) existed in my hash map. If it did, it meant a subarray summing to k had just been completed. *Frequency Mapping: Used a dictionary to store how many times each prefix sum occurred, ensuring every valid subarray was counted. *Handled the Edge Case: Initialized the map with {0: 1} to correctly catch subarrays that sum to k starting right from index 0. 🧠 Key Takeaway: *Space-Time Optimization: By using O(n) space for the hash map, I eliminated the need for nested loops, drastically speeding up the execution. *Mathematical Insight: Reinforced the logic that Sum(i, j) = PrefixSum(j) - PrefixSum(i-1). This is a powerhouse pattern for any range-sum problem. One step closer to mastering Data Structures and Algorithms! 💻🔥 The logic is getting sharper every day! 📈🤝 #100DaysOfCode #DSA #Python #ProblemSolving #StriverA2ZSheet #CodingJourney

🚀 Cracked a Classic Sliding Window Problem on LeetCode! Today I solved Maximum Average Subarray I (LeetCode #643) — a perfect example of how optimizing brute force can drastically improve performance. 💡 Problem Summary: Given an array nums and an integer k, find the maximum average of any subarray of size k. ❌ Brute Force Approach: Generate all subarrays of size k Calculate sum for each Time Complexity: O(n × k) → inefficient for large inputs ✅ Optimized Approach: Sliding Window Instead of recalculating every subarray sum: Compute sum of first k elements Slide the window forward: Add next element Remove previous element Track maximum sum 👉 Core Idea: window_sum = window_sum - old_element + new_element 💻 Clean Code: def findMaxAverage(nums, k): window_sum = sum(nums[:k]) max_sum = window_sum for i in range(k, len(nums)): window_sum += nums[i] window_sum -= nums[i - k] max_sum = max(max_sum, window_sum) return max_sum / k ⚡ Complexity: Time: O(n) Space: O(1) 🧠 Key Takeaway: Sliding Window transforms problems from O(n²) → O(n) by reusing previous computations. 🔥 This pattern is widely used in: Subarray / Substring problems Longest/Shortest window problems Real-time data processing #DataStructures #Algorithms #DSA #LeetCode #CodingInterview #PlacementPreparation #SlidingWindow #Python

Once Claude understands the full context of the connector driver you're configuring, building or extending, it can generate the code directly. In this overview, Anthropic Claude creates the Python and JSON config files required to plug directly into FlexxCore, automatically generating the methods and files needed for the connector driver (called a Transformer). From there, the transformer can be run straight inside the FlexxCore runtime, creating a mirror of what was generated in the Claude session. The SDA runtime environment includes a browser-based studio editor - similar to VS Code - where engineers can save changes, hot reload, and test the driver in real time. Using the FlexxCore API endpoints, engineers can test the connector, identify errors, and feed that information back into the Claude session to iterate and refine the integration. The result: a fully functional machine interfacing connector driver - often built in hours - that's reusable which dramatically reduces the time traditionally required to develop connector drivers for factory equipment. Watch the full video here: https://lnkd.in/dfuJAqtY #ManufacturingAI #IndustrialAutomation #SmartFactory #IndustrialAI #FactoryAutomation

🚀 DSA Challenge – Day 194 Problem: Largest Component Size by Common Factor 🔗🔢 Today’s problem is a powerful application of Union-Find (Disjoint Set Union) combined with number theory (factorization). 🧠 Problem Summary You are given an array of unique positive integers nums. We construct a graph where: 👉 Each number is a node 👉 An edge exists between two numbers if they share a common factor > 1 🎯 Goal: Return the size of the largest connected component in this graph. 💡 Key Insight Instead of explicitly building the graph (which would be expensive), we use: 🔥 Union-Find to group connected numbers Key observation: 👉 If two numbers share a factor, they belong to the same component 👉 So we can connect numbers via their prime factors ⚙️ Approach 1️⃣ For each number, compute its prime factors 2️⃣ Maintain a map: factor → index of number 3️⃣ For every factor of a number: If factor already seen → union current index with previous index Else → store it in the map 4️⃣ After processing all numbers: Find the root parent of each node Count size of each component 5️⃣ Return the maximum component size 📈 Complexity Time Complexity: 👉 Factorization: ~O(n √max(nums)) 👉 Union-Find operations: ~O(n α(n)) Space Complexity: O(n) ✨ Why This Problem Is Important This problem teaches a powerful pattern: 🔥 Connect elements indirectly using shared properties Instead of: ❌ Comparing every pair (O(n²)) We use: ✅ Factor mapping + Union-Find 💡 Key Learnings: ➡️ DSU for grouping problems ➡️ Prime factorization in graph problems ➡️ Avoiding explicit graph construction This pattern appears in: ➡️ Connected components ➡️ Grouping by similarity ➡️ Graph problems with hidden relationships 🔖 #DSA #100DaysOfCode #Day194 #UnionFind #DSU #Graphs #NumberTheory #LeetCode #Algorithms #ProblemSolving #CodingChallenge #InterviewPrep #Python #SoftwareEngineering #DeveloperJourney #TechCommunity #CodingLife

🌐 Day 15: Understanding HTTP Methods in FastAPI ⚡ Continuing my FastAPI learning journey, today I focused on how APIs handle different operations using standard HTTP methods. Here’s what I explored today: ✅ GET – retrieving data from the server ✅ POST – creating new resources ✅ PUT – updating existing data ✅ DELETE – removing resources ✅ Testing different API operations using FastAPI's interactive documentation (Swagger UI) Working with these methods helped me better understand CRUD operations (Create, Read, Update, Delete), which form the backbone of most backend systems. FastAPI makes implementing these operations clean, structured, and developer-friendly, allowing APIs to be built quickly while maintaining clarity. 💡 Key takeaway: Choosing the correct HTTP method makes APIs more predictable, scalable, and easier for other developers to use. #FastAPI #Python #BackendDevelopment #APIDevelopment #DevJourney #LearningInPublic #SoftwareEngineering

I just published a python package to PyPI — DShandler Every data science project I worked on had the same painful start: → Hunt for the right cleaning function → Copy-paste it from a previous notebook → Fix the bug that only shows up on THIS dataset → Repeat for imputation, encoding, outlier handling... So I built DShandler — a stateful, chainable pipeline class that covers the entire tabular ML workflow in one place. 𝗪𝗵𝗮𝘁 𝗶𝘁 𝗱𝗼𝗲𝘀: ✅ 7 diagnostic scanners (missing values, leakage, multicollinearity, class imbalance) ✅ Smart imputation — median, KNN, or MissForest (Random Forest-based) ✅ Outlier handling — IQR, Z-score, Isolation Forest, or domain logic ✅ Skew correction + scaling + sparse memory optimization ✅ Categorical encoding — OHE, ordinal, target encoding with smoothing ✅ Feature selection — variance pruning + VIF elimination ✅ Leakproof train/test split with a fitted preprocessor ✅ Full EDA suite — distribution profiling, relationship analysis, hypothesis testing ✅ Time-series — stationarity (ADF + KPSS), ACF/PACF, ARIMA order hints 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗰𝗵𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗶𝗻 𝗼𝗻𝗲 𝗹𝗶𝗻𝗲: dsp = DataSciencePipeline(df, target_col="_") dsp.run_diagnostics().run_cleaning().run_preprocessing() X_train, X_test, y_train, y_test, preprocessor = dsp.split() Install it: pip install dshandler Git: https://lnkd.in/dzmVTGp5 This started as a personal toolkit and turned into something I'm genuinely proud of. Would love feedback from the community — what functions would you want to see next? #Python #DataScience #MachineLearning #OpenSource #PyPI #FeatureEngineering #MLOps