/Python-Interview-Cheat-Sheet

The "Python-Interview-Cheat-Sheet" repository is a comprehensive collection of Python-related resources designed to assist individuals in their preparation for technical interviews.

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Python Interview Cheat Sheet

This cheat sheet provides an overview of common methods and techniques that are essential for Python-based coding interviews. Understanding these concepts and methods will help you solve a wide range of problems and improve your problem-solving skills.

Basic Data Structures

Lists

  • append(): Add an element to the end of the list.
  • pop(): Remove and return the last element of the list.
  • insert(): Insert an element at a specified position.
  • index(): Find the index of a specific value in the list.
  • remove(): Remove the first occurrence of a specified value.
  • len(): Get the length of the list.

Sets

  • add(): Add an element to the set.
  • remove(): Remove an element from the set.
  • union(): Perform the union of two sets.
  • intersection(): Perform the intersection of two sets.
  • difference(): Find the difference between two sets.

Dictionaries

  • get(): Retrieve the value associated with a key.
  • keys(): Get a list of keys in the dictionary.
  • values(): Get a list of values in the dictionary.
  • items(): Get a list of key-value pairs in the dictionary.
  • len(): Get the number of key-value pairs in the dictionary.

Strings

  • String manipulation: Slicing, concatenation, and searching.
  • str.split(): Split a string into a list of substrings.
  • str.join(): Join a list of strings into one string.
  • str.strip(): Remove leading and trailing whitespace.
  • str.replace(): Replace occurrences of a substring with another string.

Arrays

  • Utilize Numpy and its functions for numerical operations.

Sorting and Searching

  • Sorting: sorted(), list.sort(), sorted(iterable, key=lambda x: ...).
  • Binary search: bisect.bisect(), bisect.bisect_left(), bisect.bisect_right().

Hashing

  • Use dictionaries for O(1) key-value lookup.

Recursion

  • Understand recursion and implement recursive functions.

Dynamic Programming

  • Apply techniques like memoization (caching) to optimize recursive algorithms.

Graphs

  • Understand graph representation (adjacency lists, adjacency matrices).
  • Perform Depth-First Search (DFS) and Breadth-First Search (BFS).
  • Implement shortest path algorithms (Dijkstra's, Bellman-Ford).
  • Work with Minimum Spanning Trees (Prim's, Kruskal's).

Trees

  • Handle binary trees and Binary Search Trees (BST).
  • Traverse trees (in-order, pre-order, post-order).
  • Use Depth-First Search (DFS) on trees.
  • Implement balancing algorithms for AVL trees and Red-Black trees.

Heap

  • Work with heaps and heap operations (heapify, heappush, heappop).

Greedy Algorithms

  • Understand and implement greedy strategies.

Bit Manipulation

  • Perform bitwise operations like AND, OR, XOR, and bit shifting.
  • Check for and set/clear specific bits.

Math

  • Use basic mathematical operations.
  • Generate and check prime numbers.
  • Apply modular arithmetic.

Geometry

  • Apply computational geometry algorithms if required.

Divide and Conquer

  • Break down problems into smaller subproblems and combine their solutions.

Two Pointers

  • Utilize the two-pointer technique to solve problems by moving pointers inwards.

Sliding Window

  • Efficiently process subarrays or substrings using the sliding window technique.

Regular Expressions

  • Use Python's re module for pattern matching.

Exception Handling

  • Use try, except, and finally for error handling.

Custom Data Structures

  • Implement custom data structures such as queues, stacks, and linked lists.

Functional Programming

  • Use lambda functions, map, filter, and reduce for functional programming.

Time and Space Complexity Analysis

  • Understand Big O notation and analyze the efficiency of your code.