Day 31/100 – #100DaysOfCode 🚀 Solved LeetCode #1346 – Check If N and Its Double Exist (Python). Today I practiced brute-force comparison to check whether there exist two indices i and j such that arr[i] = 2 * arr[j]. Approach: 1) Use two nested loops to check all possible pairs. 2) Ensure that i ≠ j. 3) For each pair, check if arr[i] == 2 * arr[j]. 4) If condition is satisfied, return True. 5) If no such pair is found, return False. Time Complexity: O(n²) Space Complexity: O(1) Starting with brute force helps build understanding before optimization 💪 #LeetCode #Python #DSA #Arrays #BruteForce #ProblemSolving #100DaysOfCode
LeetCode 1346: Check If N and Its Double Exist in Array
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Day 42/100 – #100DaysOfCode 🚀 Solved LeetCode #2574 – Left and Right Sum Differences (Python). Today I practiced prefix sum logic to calculate the absolute difference between left and right sums for each index. Approach: 1) Calculate the total sum of the array. 2) Initialize leftSum = 0. 3) Traverse the array. 4) For each index, compute rightSum = total - leftSum - nums[i]. 5) Calculate the absolute difference and append it to the result. 6) Update leftSum by adding nums[i]. Time Complexity: O(n) Space Complexity: O(n) Understanding prefix sum helps solve problems efficiently 💪 #LeetCode #Python #DSA #Arrays #PrefixSum #ProblemSolving #100DaysOfCode
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Day 55/100 – #100DaysOfCode 🚀 Solved LeetCode #205 – Isomorphic Strings (Python). Today I practiced hashmap (dictionary) usage to check whether two strings follow the same pattern. Approach: 1) Create two hashmaps to store character mappings in both directions. 2) Traverse both strings together using zip(). 3) Check if the current mapping is consistent in both maps. 4) If any mismatch is found, return False. 5) Otherwise, update the mappings and continue. 6) If all mappings are valid, return True. Time Complexity: O(n) Space Complexity: O(n) Understanding how bidirectional mapping ensures consistency 💪 #LeetCode #Python #DSA #HashMap #Strings #ProblemSolving #100DaysOfCode
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Here’s a tiny Python change that pays off fast. Python tip: use `@dataclass(slots=True)` for high-volume models. It removes per-instance `__dict__`, which usually means lower memory usage and slightly faster attribute access. Great for DTOs, parser outputs, event payloads, and cache objects where shape is fixed. Mini rule: if the object schema is stable, add `slots=True` by default. #Python #Performance #CodeQuality #SoftwareEngineering
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Day 39/100 – #100DaysOfCode 🚀 Solved LeetCode #2215 – Find the Difference of Two Arrays (Python). Today I practiced set operations to efficiently find distinct elements between two arrays. Approach: 1) Convert both arrays into sets to remove duplicates. 2) Find elements present in nums1 but not in nums2 using set difference. 3) Find elements present in nums2 but not in nums1. 4) Convert both results back to lists. 5) Return the final list of differences. Time Complexity: O(n + m) Space Complexity: O(n + m) Understanding how set operations simplify comparison problems 💪 #LeetCode #Python #DSA #Sets #Arrays #ProblemSolving #100DaysOfCode
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Python Challenge – Can you solve this? Today was all about deep-diving into Lists vs. Sets and I came across a common mistake that we can sometimes overlook. Let’s test your Python understanding👇 numbers = [1, 2, 3] numbers.append([4, 5]) print(len(numbers)) A) 3 B) 4 C) 5 D) Error It’s a classic interview question that tests if you truly understand how Python handles memory and lists. Day 15/30 #30DaysOfCode #DataStructures #Day15 #PythonQuiz
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Type check your Python codebase 15x faster with Pyrefly ⚡ Tools like MyPy and Pyright process files sequentially, so larger codebases lead to longer wait times. Pyrefly, Meta's Rust-based type checker, runs checks in parallel, keeping performance nearly constant as your codebase grows. Key features: • Re-checks only changed modules for faster incremental runs • Automatically infers types for variables and return values On the PyTorch codebase, Pyrefly completes a full check in 2.4 seconds, about 15x faster than Pyright and 20x faster than MyPy. --- 📬 I share 2 practical tips on practical tools for data and AI twice a week on Substack. Subscribe here: https://bit.ly/46fdOPl #Python #TypeChecking #Rust
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Most implementations of the State pattern in Python look very “clean”. Lots of small classes. A base interface. One class per state. But if you’ve ever worked with one in a real project, you know the downside: transitions are scattered, behaviour is hard to see in one place, and adding new states often means touching multiple files. In today’s video, I rebuild the State pattern in a very different way. Instead of relying on inheritance, I make the state machine explicit as data and use decorators to define transitions. The result is a small, reusable engine where the entire flow becomes visible at a glance. If you’re interested in writing Python that’s easier to reason about and extend, this is a pattern worth understanding. 👉 Watch here: https://lnkd.in/eg22yEHR. #python #softwaredesign #designpatterns #statemachine #cleancode
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Having a tough time remembering #Python strptime vs. strftime? - The "p" (strptime) is for parsing, getting a datetime from a string - The "f" (strftime) is for formatting, getting a string from a datetime For help remembering the format codes, turn to https://buff.ly/oEdYGMI !
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Day 50/100 – #100DaysOfCode 🚀 Solved LeetCode #28 – Find the Index of the First Occurrence in a String (Python). Today I practiced string matching using a brute-force approach to find the first occurrence of a substring. Approach: 1) Traverse the main string (haystack). 2) For each index, try to match the substring (needle). 3) Compare characters one by one. 4) If all characters match, return the starting index. 5) If mismatch occurs, break and move to the next index. 6) If no match is found, return -1. Time Complexity: O(n × m) Space Complexity: O(1) Understanding basic string matching techniques step by step 💪 #LeetCode #Python #DSA #Strings #ProblemSolving #100DaysOfCode
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