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This is how the Dijkstra algorithm works.

It's a pathfinding method used to find the shortest route between nodes in a graph. 🗺️

1. Start at the source node.
2. Assign distance 0 to source, infinity to others.
3. Mark source as visited.
4. Select the unvisited node with the smallest distance.
5. Update neighbors' distances if a shorter path is found.
6. Repeat until all nodes are visited.

Key points:
- Greedy approach ✅
- No negative weights allowed ⚠️
- Time complexity: O((V + E) log V) 🕒

#Dijkstra #Algorithms #Pathfinding #ComputerScience #GraphTheory #TechEducation

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Catch a useful trick for working with division in Python 🐍

divmod() takes two numbers and in a single operation returns a tuple with the quotient and remainder from the division 📊

#Python #Coding #Programming #Tech #Tips #Dev

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Smart counting of elements using collections.Counter 📊

from collections import Counter

# Initial list with duplicate elements
logs = ["error", "info", "error", "warning", "error", "info"]

# 1. Instantly count the number of occurrences
count_dict = Counter(logs)
print(count_dict)  # Counter({'error': 3, 'info': 2, 'warning': 1})

# 2. Get the most frequent elements (Top-2)
print(count_dict.most_common(2))  # [('error', 3), ('info', 2)]

# 3. Set math for counters
clicks_day1 = Counter(item=4, banner=2)
clicks_day2 = Counter(item=1, banner=5)
# Combine the results of two days in a single operation
print(clicks_day1 + clicks_day2)  # Counter({'banner': 7, 'item': 5})

Forget about manual loops and dictionaries 🚫🔄

When you need to count the frequency of words in a text, the distribution of log types, or popular products in a store, developers usually create an empty dictionary and write a loop with a check if key not in dict: dict[key] = 1. The Counter class takes all this dirty work on itself and makes it as efficient as possible.

— Automatic initialization: You no longer need to check if a key exists in the dictionary. If the element is not there, Counter will not throw a KeyError, but simply return 0. 🛡️

— Finding leaders without sorting: The most_common(k) method returns a list of the k most frequently occurring elements. Under the hood, Python uses optimized heap algorithms, which work much faster than a full dictionary sort via sorted(). 🏆

— Mathematical operations: You can add, subtract, intersect, and merge Counter objects. This turns them into a powerful tool for aggregating metrics and analytics from different data sources in a few lines of code. ➕➖

#Python #DataScience #Coding #Programming #Automation #DevOps

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