Class 12 Informatics PracticesChapter Notes

Key Points to Remember

• →Pandas: Python library for data manipulation. import pandas as pd.
• →Series: 1D labelled array. pd.Series([10,20,30], index=['a','b','c']).
• →DataFrame: 2D labelled table. pd.DataFrame({'Name':['A','B'], 'Marks':[80,90]}).
• →Attributes: df.shape (rows, cols), df.dtypes, df.index, df.columns, df.size.
• →Methods: df.head(n), df.tail(n), df.info(), df.describe() (statistics for numeric columns).
• →Reading CSV: pd.read_csv('file.csv'). Saving: df.to_csv('file.csv', index=False).
• →Accessing: df['colName'] (Series), df[['col1','col2']] (DataFrame), df.loc['rowLabel'], df.iloc[rowIndex].

Exam Tips

Key Points to Remember

• →Boolean indexing: df[df['Marks'] > 80] — filter rows where condition is True.
• →Sorting: df.sort_values(by='Marks', ascending=True/False). df.sort_index(ascending=True).
• →Adding column: df['Grade'] = df['Marks'].apply(lambda x: 'A' if x>=90 else 'B').
• →Dropping: df.drop('colName', axis=1) for column; df.drop(rowLabel, axis=0) for row.
• →Groupby: df.groupby('Dept')['Salary'].mean() — average salary per department.
• →Merge: pd.merge(df1, df2, on='key', how='inner'). how options: 'inner','left','right','outer'.
• →Concatenate: pd.concat([df1, df2], axis=0) row-wise; axis=1 column-wise.
• →Handling missing: df.isnull() — boolean. df.fillna(value) — replace NaN. df.dropna() — remove rows with NaN.

Exam Tips

Key Points to Remember

• →import matplotlib.pyplot as plt. Always end with plt.show().
• →Line chart: plt.plot(x, y, color='red', marker='o', linestyle='--', label='Data'). Use plt.legend().
• →Bar chart: plt.bar(x, height, color='blue', width=0.5). plt.barh() for horizontal.
• →Scatter plot: plt.scatter(x, y, color='green', marker='*').
• →Pie chart: plt.pie(values, labels=labels, autopct='%1.1f%%', explode=(0.1,0,0,...)).
• →Histogram: plt.hist(data, bins=10, color='orange', edgecolor='black').
• →Formatting: plt.xlabel('X label'), plt.ylabel('Y label'), plt.title('Title'), plt.grid(True).
• →Saving: plt.savefig('chart.png') — must be called before plt.show().

Exam Tips

Key Points to Remember

• →DDL: CREATE DATABASE db; USE db; CREATE TABLE emp (eid INT PRIMARY KEY, ename VARCHAR(20), salary DECIMAL(10,2));
• →ALTER TABLE emp ADD COLUMN dept VARCHAR(20); ALTER TABLE emp MODIFY salary FLOAT; ALTER TABLE emp DROP COLUMN dept;
• →DML: INSERT INTO emp VALUES (1,'Ramesh',50000); UPDATE emp SET salary=55000 WHERE eid=1; DELETE FROM emp WHERE eid=1;
• →DQL: SELECT * FROM emp; SELECT ename, salary FROM emp WHERE salary > 40000 ORDER BY salary DESC;
• →Aggregate: SELECT dept, COUNT(*), AVG(salary), MAX(salary), MIN(salary) FROM emp GROUP BY dept HAVING AVG(salary) > 40000;
• →INNER JOIN: SELECT e.ename, d.dname FROM emp e INNER JOIN dept d ON e.dept_id = d.dept_id;
• →Constraints: PRIMARY KEY (unique, not null), FOREIGN KEY (references another table), UNIQUE, NOT NULL, DEFAULT, CHECK.

Exam Tips

Key Points to Remember

• →Network types: LAN (building/campus, high speed, owned), MAN (city-wide), WAN (global, public/leased lines).
• →Topologies: Bus (all connected to one cable), Star (all connected to central hub/switch), Ring (circular), Mesh (every device connected to every other), Tree (hierarchical).
• →IP address: IPv4 = 32-bit (e.g. 192.168.1.1), IPv6 = 128-bit hexadecimal.
• →DNS: Domain Name System — translates domain names (google.com) to IP addresses.
• →Protocols: HTTP/HTTPS (web), FTP (file transfer), SMTP (email sending), POP3/IMAP (email receiving), TCP/IP (data transmission), UDP (fast, connectionless).
• →Cloud computing: IaaS (Virtual machines — AWS EC2), PaaS (development platform — Google App Engine), SaaS (applications — Gmail, Dropbox).
• →Cybersecurity: firewall, encryption, digital signature, VPN, antivirus, two-factor authentication.

Exam Tips

Key Points to Remember

• →AI: simulation of human intelligence — speech recognition, image recognition, decision making. Types: Narrow AI (current), General AI (hypothetical), Super AI (future).
• →Machine Learning: systems learn from data. Types: Supervised (labelled data, classification/regression), Unsupervised (unlabelled, clustering), Reinforcement (reward-based).
• →Deep Learning: neural networks with many layers. Used in image recognition, NLP.
• →IoT (Internet of Things): physical devices embedded with sensors connected to internet. Examples: smart thermostat, fitness bands, smart traffic lights.
• →Big Data: 3Vs — Volume (huge size), Velocity (generated fast), Variety (structured/unstructured/semi-structured).
• →Blockchain: distributed ledger. Each block = data + hash + previous block's hash. Decentralised, tamper-resistant. Used in cryptocurrency, supply chain.
• →5G: 5th generation mobile network. Speed up to 20 Gbps. Low latency. Enables IoT at scale, autonomous vehicles.

Exam Tips

Key Points to Remember

• →Digital footprint: data trail left by online activities. Active (deliberate — posts, emails) vs Passive (unintentional — browsing history, cookies).
• →Cyber crimes: phishing (fake emails to steal credentials), vishing (voice phishing), smishing (SMS phishing), identity theft, cyberbullying, cyberstalking.
• →Safe practices: strong passwords (uppercase + lowercase + numbers + symbols, 8+ chars), 2FA, regular software updates, avoid public Wi-Fi, verify sender before clicking links.
• →E-waste: discarded electronics. Contains toxic materials (lead, mercury, cadmium). Should be sent to certified recyclers. Growing concern.
• →Intellectual property: copyright (creative works), patents (inventions), trademarks (brand identity). Plagiarism is academic dishonesty.
• →Gender and disability: technology should be accessible. Screen readers for visually impaired, voice recognition for motor impaired. Gender gap in tech employment.

Exam Tips