Algorithms & data structures
Classic patterns — search, sort, stacks, queues, and practical complexity-aware code.
Bucket Numbers into Histogram Bin Counts in Python
Partition a list of numbers into equal-width histogram bins and count how many fall into each bin using only the Python standard library.
from collections import Counter
def histogram_bins(numbers, num_bins):
"""Bucket numbers into histogram bin counts."""
if not numbers:
return []
min_val = min(numbers)
max_val = max(numbers)
bin_width = (max_val - min_val) / num_bins
# Handle edge case where all values are id…Count Smaller Elements to the Right in Python
Return a list where each index counts how many elements to its right are smaller than that element using a clean O(n²) nested-loop approach.
def count_smaller_elements(arr):
"""
Return a list where result[i] is the number of elements
to the right of arr[i] that are smaller than arr[i].
"""
result = []
for i in range(len(arr)):
count = 0
for j in range(i + 1, len(arr)):
if arr[j] < arr[i]:
…Find All Indices of a Target Value in a Python List
Returns a list of all indices where a given target value appears in a Python list using a list comprehension with enumerate.
def find_all_indices(arr, target):
return [i for i, value in enumerate(arr) if value == target]
if __name__ == "__main__":
sample_list = [4, 2, 7, 2, 9, 2, 1, 2]
target = 2
result = find_all_indices(sample_list, target)
print(result)Find Elements Appearing More Than n/3 Times in Python
Return all elements that occur more than len(array)/3 times using a simple dictionary counter.
def majority_third(arr):
"""Return elements appearing more than len(arr)/3 times."""
cutoff = len(arr) / 3
counts = {}
for x in arr:
counts[x] = counts.get(x, 0) + 1
return [x for x, c in counts.items() if c > cutoff]
if __name__ == "__main__":
test1 = [3, 2, 3]
test2 = [1, 1, 1, …Find Single Number Appearing Once in Python
Count frequency of each number in a list and return the one that appears exactly once when all others appear twice.
from collections import Counter
def find_single_number(nums):
counts = Counter(nums)
for num, count in counts.items():
if count == 1:
return num
return None
if __name__ == "__main__":
nums = [4, 1, 2, 1, 2]
result = find_single_number(nums)
print(f"Single number in {nums} …How to Compute the Cartesian Product of Two Lists in Python
Generates all ordered pairs from two lists using itertools.product and prints each combination.
from itertools import product
# Two small input lists
list_a = [1, 2, 3]
list_b = ["x", "y"]
# Compute the Cartesian product
result = list(product(list_a, list_b))
# Display the result
print("Cartesian product of", list_a, "and", list_b, "is:")
for pair in result:
print(pair)How to Generate Permutations of Length r in Python
Generate and print all r-length permutations of a list using Python's itertools.permutations.
from itertools import permutations
def show_permutations(items, r):
result = list(permutations(items, r))
for perm in result:
print(perm)
print(f"Total: {len(result)}")
if __name__ == "__main__":
data = ["A", "B", "C"]
show_permutations(data, 2)How to Get All Combinations of a List in Python
Generate and display all combinations of a given length from a list using Python's itertools.combinations.
from itertools import combinations
def list_combinations(items, r):
"""Return all combinations of length r from a list."""
return list(combinations(items, r))
if __name__ == "__main__":
fruits = ["apple", "banana", "cherry", "date"]
pick = 2
result = list_combinations(fruits, pick)
print…How to Heapify a List into a Min Heap with heapq in Python
Convert any list into a valid min heap in-place using Python's heapq.heapify(), then pop the smallest element to verify heap order.
import heapq
data = [5, 3, 8, 1, 9, 2, 7, 4, 6]
print("Original list:", data)
heapq.heapify(data)
print("Min heap:", data)
popped = heapq.heappop(data)
print("Smallest element popped:", popped)
print("Heap after pop:", data)How to Replace Outliers Beyond Threshold with Cap in Python
Replace values that fall below a lower threshold or above an upper threshold by capping them to the threshold values using a simple Python function.
def replace_outliers_with_cap(data, lower_threshold=None, upper_threshold=None):
"""Replace values beyond given thresholds with the threshold values (capping)."""
if lower_threshold is None and upper_threshold is None:
raise ValueError("At least one threshold must be provided.")
capped_data = …How to Sort Array by Parity (Even Before Odd) in Python
Rearrange an array so all even numbers appear before all odd numbers using a simple two-list partition approach.
def sort_array_by_parity(nums):
"""
Rearrange the array so that all even integers come first,
followed by all odd integers. The order within even and odd
groups is not required to be sorted.
"""
even = []
odd = []
for num in nums:
if num % 2 == 0:
even.append(nu…Move Zeroes to End in Python Maintaining Order
In-place algorithm that moves all zeroes to the end of a list while preserving the relative order of non-zero elements.
def move_zeroes(nums):
non_zero_index = 0
for i in range(len(nums)):
if nums[i] != 0:
nums[non_zero_index], nums[i] = nums[i], nums[non_zero_index]
non_zero_index += 1
return nums
if __name__ == "__main__":
example = [0, 1, 0, 3, 12]
result = move_zeroes(example)
…Segregate Negative Numbers Before Positives in Python
Reorders a list so all negative numbers appear before non-negative numbers while preserving the original relative order of elements.
def segregate_negatives(numbers):
"""Segregate negatives before positives without altering relative order."""
negatives = [n for n in numbers if n < 0]
positives = [n for n in numbers if n >= 0]
return negatives + positives
if __name__ == "__main__":
sample = [3, -1, 4, -5, 2, -9, 0]
result =…Sort Unique Values by Frequency in Python
Count element frequencies with Counter and sort unique values by descending frequency, breaking ties alphabetically.
from collections import Counter
def sort_unique_by_frequency(values):
counts = Counter(values)
return sorted(counts.keys(), key=lambda x: (-counts[x], x))
if __name__ == "__main__":
data = [4, 2, 2, 8, 3, 3, 1, 3, 5, 5, 5, 5, 1]
result = sort_unique_by_frequency(data)
print(f"Sorted unique values…Browse by section
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