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Unit 1-Python Programming II

School Revise · Class 12 Artificial Intelligence · Unit 1

Python Programming II

Code 843, Class 12. AI needs strong Python and data skills. Here we use functions, NumPy and Pandas to handle data.

NumPy

number arrays

Pandas

data tables

What this unit is about

We build on Python with functions, and the NumPy and Pandas libraries that AI uses to work with numbers and data.

1. Functions and lists

A function is reusable code that can return a result. Lists and loops process many values, and a list comprehension builds a list in one line.

squares = [x * x for x in range(1, 4)] print(squares) Output: [1, 4, 9]

2. NumPy

NumPy works with arrays of numbers. np.array([…]) makes an array, and a.sum(), a.mean() and a.max() summarise it quickly.

3. Pandas

Pandas handles data in tables. A DataFrame is a table; df.groupby summarises groups and df[“col”].mean() gives an average, ready for analysis.

Practise with the interactive

Explore the idea by tapping. The interactive opens right here in the lesson.

Try it in the code lab

Write and run real Python right here in the lesson, then work through the practice problems with answers.

Starting Python...


Coding practice problems, with answers


Type each one into the code lab above, then open the card to see the worked solution and its output.



Problem 1. Write a function that adds two numbers.



SOLUTION


def add(a, b): return a + b print(add(5, 3))


OUTPUT


8







Problem 2. Build a list of squares in one line.



SOLUTION


squares = [x * x for x in range(1, 4)] print(squares)


OUTPUT


[1, 4, 9]







Problem 3. Make a NumPy array and sum it.



SOLUTION


import numpy as np a = np.array([1, 2, 3, 4]) print(a.sum())


OUTPUT


10







Problem 4. Find the mean and max with NumPy.



SOLUTION


import numpy as np a = np.array([2, 4, 6]) print(a.mean(), a.max())


OUTPUT


4.0 6







Problem 5. Make a Pandas DataFrame.



SOLUTION


import pandas as pd df = pd.DataFrame({“marks”: [80, 90, 70]}) print(df[“marks”].mean())


OUTPUT


80.0







Problem 6. Group and average with Pandas.



SOLUTION


import pandas as pd df = pd.DataFrame({“cls”: [“A”, “A”, “B”], “m”: [80, 90, 70]}) print(df.groupby(“cls”)[“m”].mean().tolist())


OUTPUT


[85.0, 70.0]







Problem 7. Filter rows in a DataFrame.



SOLUTION


import pandas as pd df = pd.DataFrame({“m”: [80, 40, 90]}) print(df[df[“m”] >= 50][“m”].tolist())


OUTPUT


[80, 90]







Problem 8. Sum a list with a loop.



SOLUTION


nums = [10, 20, 30] s = 0 for x in nums: s = s + x print(s)


OUTPUT


60







Problem 9. Handle a division error safely.



SOLUTION


try: print(10 / 0) except ZeroDivisionError: print(“cannot divide by zero”)


OUTPUT


cannot divide by zero







Problem 10. Count values in a Pandas column.



SOLUTION


import pandas as pd df = pd.DataFrame({“m”: [80, 90, 70]}) print(df[“m”].count())


OUTPUT


3






Practice set A, multiple choice



1. Which library works with number arrays?



NumPy.







2. Which handles data tables?



Pandas.







3. Which builds a list in one line?



A list comprehension.







4. Which Pandas method summarises groups?



groupby().






Quick summary








Python for AI uses functions (reusable code that returns a result), list comprehensions (build a list in one line), and libraries. NumPy works with arrays of numbers (sum, mean, max). Pandas handles data in tables: a DataFrame holds rows and columns, groupby summarises groups, and mean gives averages, ready for analysis.












Open the Virtual Lab








These free Class 12 Artificial Intelligence (Code 843) notes explain Python programming for AI, functions and list comprehensions, and the NumPy and Pandas libraries with groupby with clear examples and practice, for CBSE students across India and the Gulf including the UAE, Saudi Arabia, Qatar, Oman, Kuwait and Bahrain.






© 2026 School Revise. All rights reserved. Original content aligned to the CBSE and NCERT Class 12 Artificial Intelligence syllabus. Unauthorised copying is not permitted.





	

	
	


	

		

					

			



		

			

	

	

		

			

				

		

	






	

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