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. ➕➖

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📂 Reminder on Python set — set methods!

For example, add() adds an element to the set, update() combines several elements, and intersection() helps quickly find common values between data sets.

In the picture — the main methods and operations for working with set: addition, removal, union, intersection, difference, and set checks.

Save it to not lose it!

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What are lambda functions in Python?

Lambda functions — are anonymous functions, which are created using the lambda keyword. They can accept any number of arguments, but contain only one expression, the result of which is automatically returned.

They are most often used for short operations, for example during sorting, filtering or data processing.

# Example:
sorted_data = sorted(data, key=lambda x: x[1])
filtered_data = list(filter(lambda x: x > 0, numbers))

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