Python

• Control Flow & Loops: Implemented a for loop combined with if-elif-else conditional logic to evaluate numbers from 1 to 100.

• Modulo Operators: Evaluated divisibility conditions (%) to handle multiple criteria seamlessly:

  • Numbers divisible by both 3 and 5 output "fizzbuzz".

  • Numbers divisible by 3 output "fizz".

  • Numbers divisible by 5 output "buzz".

  • All remaining integers print as standard values.

Day 3: Data Analysis with Pandas (student.csv)

Data Exploration: Mounted Google Drive, imported Pandas (import pandas as pd), loaded data (read_csv), and inspected structures using .head(), .info(), and .describe().

Selection & Manipulation: Indexed specific columns (df[['name', 'mark']]), sliced rows via .iloc[], applied conditional logic to compute boolean pass flags (mark >= 60), and renamed/dropped columns.

Aggregation & Pivot Tables: Grouped datasets (.groupby()) to compute mean scores and count distributions by class/gender. Constructed multi-variable pivot tables and assigned letter grades (A–D) using apply() and lambda expressions.

Data Export: Exported refined DataFrame results directly to a structured .csv file via .to_csv().