Assam HS Skill Subject Artificial Intelligence: Python Programming for AI

What the exam tests

Python questions here are applied: can you choose the right data type, trace a loop or function, and use NumPy and pandas to load, filter, and summarise a dataset without writing a manual loop?

Core concepts

• Core types: int, float, str, bool. Collections: list (ordered, mutable), tuple (ordered, immutable), dict (key–value pairs), set (unique items).
• Control flow: if/elif/else, for over a range or iterable, while with a changing condition, plus break and continue.
• Functions: defined with def, take parameters, return values; default and keyword arguments; local versus global scope.
• NumPy: the ndarray stores same-type values and supports vectorised maths. a * 2 multiplies every element and is far faster than a Python loop. Also shape, reshape, broadcasting, mean(), sum().
• pandas: a Series is a labelled 1-D array; a DataFrame is a labelled table. Useful calls: read_csv(), head(), info(), describe(), boolean filtering, groupby(), fillna(), dropna().

Worked example

import pandas as pd
df = pd.read_csv('marks.csv')
print(df[df['score'] > 40]['score'].mean())

Reasoning: df['score'] > 40 produces a True/False Series; df[...] keeps only rows where it is True; the second ['score'] selects that column; .mean() averages the values, skipping missing ones by default. The output is the average score of students scoring above 40.

Traps that cost marks

• Indentation errors - Python uses indentation, not braces, to define blocks.
• Using = (assignment) where == (comparison) is needed.
• Expecting 7 / 2 to give 3; it gives 3.5, while 7 // 2 gives 3.
• Editing a list while looping over it, which silently skips elements.
• Treating a NumPy array like a list: a + 1 adds 1 to every element rather than appending.
• Forgetting that pandas column names are case-sensitive.