CBSE Class 12 Informatics Practices

Class 12 · CBSEComputer Science32 notes in 4 folders, 163 KB

Notes for CBSE Class 12 Informatics Practices (065), in folders for the curriculum's four units in order: data handling with Pandas and Matplotlib, SQL queries, computer networks and societal impacts. Code is in Python and MySQL, with worked examples. Follows the 2026-27 curriculum. The project and practical file are not covered. Delete any folder your course leaves out once the notes are yours.

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What is inside

Data handling using Pandas and data visualisation
- Pandas, NumPy and Matplotlib3 KB
- Creating a Series, its attributes and arithmetic7 KB
- Selecting, indexing and slicing a Series3 KB
- Creating a DataFrame, its attributes and iteration7 KB
- Rows, columns and indexing a DataFrame8 KB
- CSV files: importing and exporting4 KB
- Summarising data: sorting, duplicates, quantiles and grouping4 KB
- Line plots, labels, legends and saving plots7 KB
- Bar graphs and histograms5 KB
Database query using SQL
- Database concepts, keys and the kinds of SQL statement5 KB
- Creating databases and tables, data types, constraints, ALTER and DROP5 KB
- INSERT, UPDATE and DELETE4 KB
- SELECT queries: WHERE, operators, LIKE, NULL and DISTINCT6 KB
- Math functions: POWER, ROUND and MOD4 KB
- Text functions4 KB
- Date functions4 KB
- Aggregate functions and COUNT4 KB
- GROUP BY, HAVING and ORDER BY4 KB
- Working with two tables: Cartesian product and equi-join5 KB
Introduction to computer networks
- Networks and their types: PAN, LAN, MAN and WAN5 KB
- Network devices5 KB
- Network topologies4 KB
- The internet, URL, WWW, email, chat and VoIP5 KB
- Websites, web pages, web servers and hosting5 KB
- Web browsers, settings, add-ons, plug-ins and cookies5 KB
Societal impacts
- Digital footprint, net and communication etiquette5 KB
- Data protection and privacy5 KB
- Intellectual property, plagiarism, copyright and licensing7 KB
- Free and open source software4 KB
- Cybercrime and the Indian IT Act7 KB
- E-waste: hazards and management5 KB
- Technology and health, gender and disability7 KB

The first note

Data handling using Pandas and data visualisation / Pandas, NumPy and Matplotlib

Python's standard library handles numbers, text and lists, but it has nothing for working with a table of data or for drawing a chart. Two third-party libraries fill those gaps, and a third sits underneath one of them. | Library | What it is for | Usual import | |---|---|---| | **Pandas** | Holding and analysing labelled data in one dimension (Series) or two (DataFrame) | `import pandas as pd` | | **NumPy** | Fast arrays of numbers, which Pandas is built on | `import numpy as np` | | **Matplotlib** | Drawing line plots, bar graphs, histograms and other charts | `import matplotlib.pyplot as plt` | The names `pd`, `np` and `plt` are aliases. They are conventions, not requirements, but every example in this course uses them, and code written with a different alias must use that alias everywhere. A library has to be installed once on the computer (with `pip install pandas`) and imported in every program that uses it. ## Pandas Pandas (the name comes from "panel data") reads data from files such as CSV, keeps it in labelled structures, and offers operations that would take many lines of plain Python: selecting rows by a condition, adding columns, grouping and summarising, and handing the result to Matplotlib for plotting. It has two main data structures. - A **Series** is a one-dimensional labelled array. It holds values of one data type and has an index that labels each value. - A **DataFrame** is a two-dimensional labelled structure, a table with rows and columns. Each column…

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