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Articles on Trending Technologies
Technical articles with clear explanations and examples
Python - Non-overlapping Random Ranges
Generating non-overlapping random ranges is useful in data analysis, sampling, and testing scenarios. Python's random module provides tools to create ranges that don't intersect with each other. Understanding the Problem Given a start value, end value, and number of ranges needed, we generate random ranges that don't overlap. For example, with start=1, end=50, and 3 ranges, we might get [(5, 8), (15, 22), (35, 40)]. Method 1: Simple Range Generation This approach generates random ranges but doesn't guarantee they won't overlap ? import random def simple_ranges(start, end, num_ranges): ranges ...
Read MorePython - Non-Overlapping occurrences of N Repeated K character
In this article, we'll find the non-overlapping occurrences of N repeated K characters using Python. This is a common string processing problem where we need to count how many times a specific character appears consecutively a given number of times. Understanding the Problem Given a string, we need to find non-overlapping occurrences where character K appears exactly N consecutive times. For example, in string "AABBCCAAA", if K="A" and N=2, we look for "AA" patterns that don't overlap. String: AABBCCAAA Looking for K='A' repeated N=2 times AA ...
Read MorePython - Non-None elements indices
The problem at hand is to get the indices of non-None elements in a given input list. This is useful when working with datasets containing missing or empty values represented as None. Understanding the Problem Given a list containing some None values, we need to find the indices of all non-None elements. For example, given the list [1, None, 5, None, 8, 9], we should return [0, 2, 4, 5] as these are the positions of non-None values. Method 1: Using a For Loop The basic approach iterates through the list and checks each element ? ...
Read MorePython - Nested Records List from Lists
The problem at hand is that we have to create an algorithm for getting nested records lists from given multiple lists with the help of Python. Sometimes we need to combine given lists for a reason in real life applications, so this problem will be helpful to solve those scenarios. Understanding the Logic In this problem we will be given two or more lists and we have to combine them to form nested records lists by applying different approaches. We will explore three methods: using the zip() function, combining loops with zip(), and using user−defined functions. Method ...
Read MorePython - Nested List to single value Tuple
In Python, converting a nested list to a single-value tuple means extracting all elements from sublists and wrapping each element in its own tuple. This operation is useful in data processing and competitive programming scenarios. Understanding the Problem We need to convert a nested list like [[1, 2], [3, 4]] into single-value tuples like [(1, ), (2, ), (3, ), (4, )]. Each element becomes a tuple containing only that single value. Method 1: Using reduce() Function The reduce() function from functools can flatten the nested list, then we create single-value tuples ? from ...
Read MorePython - Nearest K Sort
The nearest K sort problem requires sorting a list of elements based on their absolute difference from a given value K. Elements with smaller differences appear first in the sorted result. Understanding the Problem We need to sort list items by their increasing distance from value K. For each element, we calculate |element - K| and arrange elements based on minimum difference first. Nearest K Sort Example (K = 10) Original: 10 4 ...
Read MorePython - Multiplication across Like Keys Value list elements
In Python, when working with a list of dictionaries, you often need to perform operations across dictionaries with similar keys. One common task is multiplying values for the same keys across all dictionaries in the list. Understanding the Problem Given a list of dictionaries with identical keys, we need to multiply the values for each key across all dictionaries. For example, if we have three dictionaries with key 'A', we multiply all the values associated with 'A' together. Algorithm Step 1 − Initialize an empty dictionary to store the multiplication results Step 2 − Iterate ...
Read MorePython - Multiple Keys Grouped Summation
Multiple keys grouped summation involves grouping data by multiple keys and calculating the sum of values for each group. This is commonly used in data analysis when you need to aggregate values based on multiple criteria. Understanding the Problem In multiple keys grouped summation, we have tuples where the first element is a value and the remaining elements form a composite key. Our task is to group tuples with the same composite key and sum their values. For example, given data like (1000, 2022, 1), we treat (2022, 1) as the key and 1000 as the value ...
Read MorePython - Multiple Column Sort in Tuples
In Python, sorting tuples by multiple columns means organizing data based on several criteria in priority order. Python provides elegant solutions using the sorted() function with custom sorting keys. Understanding Multiple Column Sorting When sorting tuples by multiple columns, Python first sorts by the first specified column, then by the second column for ties, and so on. Each tuple represents a row of data ? # Example: Sort by first column, then by second column data = [(5, 8), (4, 6), (2, 5), (7, 9), (5, 3)] sorted_data = sorted(data) print("Original:", data) print("Sorted:", sorted_data) ...
Read MorePython - Multimode of List
In Python, finding the multimode of a list means identifying all elements that occur with the highest frequency. Unlike mode (single most frequent element), multimode returns all elements tied for the highest count. Using statistics.multimode() Python's statistics module provides a built-in multimode() function that returns all most frequent elements ? import statistics numbers = [9, 8, 8, 7, 7, 7, 6, 6, 6, 6] result = statistics.multimode(numbers) print("Multimode:", result) print("Most frequent element appears:", numbers.count(result[0]), "times") The output of the above code is ? Multimode: [6] Most frequent element appears: 4 times ...
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