Coding Interview Resources

Data Structures and Algorithms in Real Life examples

Array :
2D arrays (matrices) are mainly used in Image Processing.
RGB image uses a 3D matrix.
2D arrays are also used in Game Design like Sudoku, Chess.
The Leader Board of game or coding contest.

Stack :
Used in backtracking, check valid parenthesis in an expression.
Evaluating Infix and Postfix expressions.
Used in Recursive function calls to store function calls and their results.
Undo and Redo operations in word processors like MS-Word, and Notepad.
Browsing history of visited websites.
Call history/log in mobile phones.
Java Virtual Machine uses a stack to store immediate calculation results.

Queue:
Windows operating system uses a circular queue to switch between different applications.
Used in First Come First Serve job/CPU scheduling algorithm which follows FIFO order.
All the requests are queued for the server to respond.

Priority Queue :
A priority queue is used in priority scheduling algorithm and interrupt handling in OS.
Used in Huffman Coding in compression algorithms.

Linked List:
Previous and Next Page in Web Browser.
Songs in the music player are linked to the previous and next songs using doubly linked list.
Next and previous images in a phone gallery.
Multiple Applications running on a PC uses circular linked list.
Used for the implementation of stacks, queues, trees, and graphs.

Graph :
In Facebook, LinkedIn and other social networking sites, users are considered to be the vertices and the edge between them indicates that they are connected.
Facebook’s Graph API and Google’s Knowledge API are the best examples of grpah.
Google Maps, Yahoo Maps, Apple Maps uses graph to show the shortest path using Breadth First Search (BFS).
Used in HTML DOM and React Virtual DOM.

Tree :
Representation structure in File Explorer. (Folders and Subfolders) uses N-ary Tree.
Auto-suggestions when you google something using Trie.
Used in decision-based machine learning algorithms.
Used in Backtracking to maintain the state-space tree.
A binary tree is used in database indexing to store and retrieve data in an efficient manner.
To implement Heap data structure.

Binary Search Trees: (BST) can be used in sorting algorithms.

Dijkstra Algorithm :
This algorithm is used to find the shortest path between two vertices in a graph in such a way that the sum of weights between the edges is minimum.

Prims Algorithm :
It is a greedy algorithm to obtain a minimum spanning tree.

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2.79K viewsedited 07:13

Easy Python scenarios for everyday data tasks

Scenario 1: Data Cleaning
Question:
You have a DataFrame containing product prices with columns Product and Price. Some of the prices are stored as strings with a dollar sign, like $10. Write a Python function to convert the prices to float.

Answer:
import pandas as pd
data = {
'Product': ['A', 'B', 'C', 'D'],
'Price': ['$10', '$20', '$30', '$40']
}
df = pd.DataFrame(data)

def clean_prices(df):
df['Price'] = df['Price'].str.replace('$', '').astype(float)
return df
cleaned_df = clean_prices(df)
print(cleaned_df)

Scenario 2: Basic Aggregation
Question:
You have a DataFrame containing sales data with columns Region and Sales. Write a Python function to calculate the total sales for each region.

Answer:
import pandas as pd
data = {
'Region': ['North', 'South', 'East', 'West', 'North', 'South', 'East', 'West'],
'Sales': [100, 200, 150, 250, 300, 100, 200, 150]
}
df = pd.DataFrame(data)

def total_sales_per_region(df):
total_sales = df.groupby('Region')['Sales'].sum().reset_index()
return total_sales

total_sales = total_sales_per_region(df)
print(total_sales)

Scenario 3: Filtering Data
Question:
You have a DataFrame containing customer data with columns ‘CustomerID’, Name, and Age. Write a Python function to filter out customers who are younger than 18 years old.

Answer:
import pandas as pd
data = {
'CustomerID': [1, 2, 3, 4, 5],
'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
'Age': [17, 22, 15, 35, 40]
}
df = pd.DataFrame(data)

def filter_customers(df):
filtered_df = df[df['Age'] >= 18]
return filtered_df
filtered_customers = filter_customers(df)
print(filtered_customers)

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2.27K views08:12

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