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Class 12 Computer ScienceChapter Notes
4 chapters · Definitions, key points, formulas & exam tips
Ch 1
Python — Exception Handling and File Handling
Key Definitions
Exception: A runtime error that disrupts normal program flow. Can be handled using try-except.
File Object: A Python object that represents an open file. Created using the open() function.
Key Points to Remember
- →Exception hierarchy: BaseException → Exception → specific exceptions (ValueError, ZeroDivisionError, FileNotFoundError).
- →try block: code that may raise exception. except: handles it. else: runs if no exception. finally: always runs.
- →File modes: 'r' (read), 'w' (write — creates/overwrites), 'a' (append), 'r+' (read+write), 'rb'/'wb' (binary).
- →Reading methods: read() — entire file as string, readline() — one line, readlines() — list of lines.
- →Writing: write(string) — no newline added automatically. writelines(list) — no newlines added.
- →Always close files: f.close() or use 'with open() as f:' context manager (auto-closes).
- →seek(n): move file pointer to position n. tell(): return current pointer position.
Formulas & Equations
with open('file.txt', 'r') as f: data = f.read()
try: ... except ExceptionType as e: ... else: ... finally: ...
Exam Tips
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Output questions on file handling: trace what's written to the file step by step.
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Binary files use pickle module: pickle.dump(obj, file) and pickle.load(file).
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CSV files: use csv.reader() and csv.writer() from the csv module.
Ch 2
Pandas — Series and DataFrame
Key Definitions
Series: A one-dimensional labelled array in Pandas. Like a column of a spreadsheet.
DataFrame: A two-dimensional labelled data structure with rows and columns. Like a spreadsheet or SQL table.
Key Points to Remember
- →Create Series: pd.Series(data, index=[]). Create DataFrame: pd.DataFrame({'col': [values]}).
- →Importing CSV: df = pd.read_csv('file.csv'). Key params: sep, header, index_col, usecols.
- →Selection: df['col'] or df[['col1','col2']] for columns. df.loc[label] for row by label. df.iloc[n] for row by position.
- →Boolean filtering: df[df['marks'] > 80].
- →Missing values: df.isnull() to check, fillna(value) to fill, dropna() to remove rows with NaN.
- →Aggregation: df['col'].sum(), mean(), max(), min(), count().
- →Grouping: df.groupby('col').agg({'col2': 'sum'}).
- →Key info methods: df.head(n), df.tail(n), df.describe(), df.info(), df.shape, df.dtypes.
Exam Tips
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loc uses labels; iloc uses integer positions — very common exam distinction.
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df.shape returns (rows, columns) as a tuple.
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For MCQs on Pandas: pd.read_csv returns a DataFrame; pd.Series creates a Series.
Ch 3
Computer Networks
Key Definitions
Protocol: A set of rules that govern data communication between devices. Examples: HTTP, FTP, TCP/IP.
IP Address: A unique numerical label assigned to each device on a network. IPv4: 32-bit. IPv6: 128-bit.
DNS (Domain Name System): Translates domain names (like www.google.com) into IP addresses.
Key Points to Remember
- →OSI model layers (bottom to top): Physical → Data Link → Network → Transport → Session → Presentation → Application.
- →TCP/IP model (4 layers): Network Access → Internet → Transport → Application.
- →Physical layer: actual bits transmitted. Data Link: frames, MAC address. Network: routing, IP address. Transport: TCP (reliable) / UDP (fast).
- →Network devices: Hub (broadcasts to all ports, layer 1), Switch (uses MAC address, layer 2, smarter), Router (connects different networks, layer 3, uses IP).
- →Common protocols: HTTP (web), HTTPS (secure web), FTP (file transfer), SMTP (send email), POP3/IMAP (receive email), DNS (name resolution), DHCP (auto IP assignment).
- →Packet switching: data broken into packets, each routed independently. More efficient than circuit switching.
- →Network types by coverage: PAN (personal) < LAN (building) < MAN (city) < WAN (country/world).
Exam Tips
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OSI model question: name all 7 layers in order — bottom to top (Physical to Application).
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Hub vs Switch vs Router: Hub = dumb (broadcasts), Switch = smart (MAC-based), Router = connects networks.
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TCP = reliable, connection-oriented (used for web, email). UDP = fast, connectionless (used for video streaming, DNS).
Ch 4
Database Concepts and SQL
Key Definitions
Primary Key: A column (or combination) that uniquely identifies each row in a table. Cannot be NULL.
Foreign Key: A column in one table that refers to the primary key of another table. Establishes relationships.
DDL / DML: DDL (Data Definition Language): CREATE, ALTER, DROP. DML (Data Manipulation Language): INSERT, UPDATE, DELETE, SELECT.
Key Points to Remember
- →RDBMS stores data in tables (relations). Each row = record, each column = field/attribute.
- →Candidate key: any minimal set that can uniquely identify rows. Primary key is chosen from candidate keys.
- →SELECT syntax: SELECT col FROM table WHERE condition GROUP BY col HAVING condition ORDER BY col ASC/DESC.
- →Aggregate functions: COUNT(*) — total rows, SUM(col) — total, AVG(col) — average, MAX/MIN(col).
- →WHERE filters before aggregation; HAVING filters after GROUP BY.
- →JOIN: INNER JOIN returns matching rows from both tables. LEFT JOIN returns all rows from left + matching from right.
- →NULL handling: IS NULL / IS NOT NULL (not = NULL or != NULL).
- →ALTER TABLE: ADD COLUMN, MODIFY COLUMN, DROP COLUMN. DROP TABLE: deletes entire table.
Formulas & Equations
SELECT col1, col2 FROM table WHERE condition ORDER BY col ASC;
SELECT dept, COUNT(*) FROM emp GROUP BY dept HAVING COUNT(*) > 5;
SELECT a.col, b.col FROM a INNER JOIN b ON a.id = b.id;
Exam Tips
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WHERE vs HAVING: WHERE filters rows (before grouping), HAVING filters groups (after GROUP BY).
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For JOIN questions: write the ON condition carefully — match primary key to foreign key.
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DISTINCT keyword: SELECT DISTINCT col — removes duplicate values from output.