ProSyllabus Journal CBSE Class 12 Board Examination Complete Summary and Solutions for Data Handling Using Pandas – I – NCERT Class XII Informatics Practices, Chapter 2 – Explanation, Questions, Answers Detailed summary and explanation of Chapter 2 'Data Handling Using Pandas – I' from the NCERT Informatics Practices textbook for Class XII. The chapter introduces Python libraries (NumPy, Pandas, Matplotlib) and focuses on Pandas as a high-level data manipulation tool built on NumPy and Matplotlib, explaining its core data structures Series and DataFrame, their creation from lists, arrays, dictionaries, and scalar values, and how to use indexing and slicing for data access. It also covers basic DataFrame operations, importing and exporting data between CSV files and DataFrames, and compares Pandas Series with NumPy ndarrays for tabular data analysis. All NCERT exercises, questions, and answers related to this chapter are included for exam-oriented practice.
Updated 11 months ago NCERT · Class XII · Informatics Practices · Chapter 2 · Python · Pandas · NumPy · Matplotlib · Data Handling · Series · DataFrame · CSV · Data Analysis · Summary · Questions · Answers · Explanation
#Data Handling Using Pandas #Pandas Series #Pandas DataFrame #NumPy #Matplotlib #Python Libraries #CSV Import Export #Tabular Data #Data Analysis #NCERT #Class 12 #Informatics Practices #Summary #Explanation #Questions #Answers #Chapter 2
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Data Handling Using Pandas - I - Class 12 Informatics Practices Chapter 2 Ultimate Study Guide 2025
Full Chapter Summary & Detailed Notes
Key Definitions & Terms
Text Book Questions & Answers
Key Concepts
Historical Development
Technique Examples
Interactive Quiz (10 Q)
Quick Revision Notes & Mnemonics
Key Terms & Processes
Processes Step-by-Step
Full Chapter Summary & Detailed Notes - Data Handling Using Pandas - I Class 12 NCERT
Overview & Key Concepts
Chapter Goal : Introduce Pandas for data manipulation/analysis. Exam Focus: Libraries (NumPy/Pandas/Matplotlib), Series creation/access/attributes/methods, NumPy vs Pandas, installation. 2025 Updates: Pandas 2.0 features (e.g., PyArrow backend), data science integration. Fun Fact: Pandas named from 'Panel Data'. Core Idea: Efficient data structures for real-world analysis.Wider Scope : From libraries to Series; sources: Activities (e.g., monuments series), Think/Reflect (alias names). Expanded: Practical code applications.Expanded Content : Point-wise for recall; add 2025 relevance like efficient large data handling.
Introduction to Python Libraries
Definition : Collections of modules for tasks without detailed code. Ex: NumPy (numerical), Pandas (analysis), Matplotlib (visualization).Purpose : Manipulate/transform/visualize data efficiently.Example : NumPy multidimensional arrays; Pandas Series/DataFrame/Panel.Expanded : Built on NumPy; Pandas for tabular data.
Conceptual Diagram: Series Structure
Index-value pairs; visualizes 1D labeled array.
Why This Guide Stands Out
Comprehensive: All code/attributes point-wise, 2025 with Pandas updates; analyzed for data tasks.
NumPy vs Pandas
Differences : 1. NumPy homogeneous arrays, Pandas heterogeneous DataFrames. 2. Pandas simpler for file/plot/select/join/GROUP BY. 3. Pandas column names for tracking. 4. Pandas tabular, NumPy numeric arrays.Do You Know? : Pandas built on NumPy/Matplotlib.Expanded : Use Pandas for mixed data types.
Exam Activities
Create series (monuments/states); count non-null.
Installing Pandas
Command : pip install pandas (requires Python/NumPy).Alias : import pandas as pd (not mandatory, but conventional).Expanded : Similar to NumPy install.
Data Structures in Pandas
Series : 1D labeled array (any type, default 0-based index).DataFrame : 2D labeled structure (tabular).Expanded : Efficient storage/retrieval/modification.
Creation of Series
From Scalars : pd.Series([values]); custom index.From NumPy Arrays : pd.Series(np.array); length match required.From Dictionary : Keys as indices.Expanded : Examples with code outputs.
Accessing Elements of Series
Indexing : Positional (0-based), Labelled (custom).Slicing : [start:end] (positional excludes end, labels include).Expanded : Modify via slicing; reverse order.
Attributes of Series
name : Assign series name.index.name : Name index.values : List of values.size : Number of elements.empty : True if empty.Expanded : Table with examples.
Methods of Series
head(n) : First n elements.tail(n) : Last n elements.Expanded : For analysis/visualization.
Summary Key Points
Pandas: Data manipulation; Series (1D), creation/access/attributes/methods. Vs NumPy: Heterogeneous/tabular.
Impact: Efficient analysis; challenges: Length errors.
Project & Group Ideas
Group: Create series from data; individual: Attribute poster.
Debate: Pandas vs NumPy in projects.
Code role-play: Slicing scenarios.
Key Definitions & Terms - Complete Glossary
All terms from chapter; detailed with examples, relevance. Expanded: 30+ terms grouped; added advanced like "Heterogeneous Data", "Labelled Index" for depth/easy flashcards.
Python Libraries
Modules for tasks. Ex: Pandas analysis. Relevance: No detailed code.
NumPy
Numerical computing arrays. Ex: Multidimensional. Relevance: Homogeneous data.
Pandas
Data manipulation tool. Ex: Series/DataFrame. Relevance: Tabular analysis.
Matplotlib
Visualization library. Ex: Plots/histograms. Relevance: Data graphs.
Series
1D labeled array. Ex: Index-value. Relevance: Any type data.
DataFrame
2D labeled table. Ex: Columns/rows. Relevance: Heterogeneous.
Positional Index
0-based integer. Ex: series[0]. Relevance: Default access.
Labelled Index
Custom labels. Ex: series['Feb']. Relevance: Meaningful.
Slicing
Extract part. Ex: [1:3]. Relevance: Subsets.
name Attribute
Series name. Ex: 'Capitals'. Relevance: Identification.
index.name
Index name. Ex: 'Countries'. Relevance: Context.
values
List of values. Ex: ['NewDelhi'...]. Relevance: Extraction.
size
Element count. Ex: 4. Relevance: Length check.
empty
True if no elements. Ex: pd.Series(). Relevance: Validation.
head()
First n elements. Ex: head(5). Relevance: Preview.
tail()
Last n elements. Ex: tail(3). Relevance: End view.
Heterogeneous Data
Mixed types. Ex: Int/string. Relevance: Pandas strength.
Homogeneous Data
Same type. Ex: All floats. Relevance: NumPy requirement.
ValueError
Length mismatch. Ex: Array/index. Relevance: Error handling.
pip install
Package installation. Ex: pandas. Relevance: Setup.
import as pd
Alias usage. Ex: pd.Series. Relevance: Convention.
ndarray
NumPy array. Ex: np.array([1,2]). Relevance: Base for Series.
dtype
Data type. Ex: int64. Relevance: Type info.
Scalar Values
Single values. Ex: pd.Series([10]). Relevance: Basic creation.
Dictionary Keys
Become indices. Ex: {'India':'NewDelhi'}. Relevance: Mapped.
Reverse Slicing
Backward order. Ex: [::-1]. Relevance: Reversal.
Modification via Slicing
Update subsets. Ex: [1:3]=50. Relevance: Bulk change.
NaN
Not a Number. Ex: np.nan. Relevance: Missing data.
Non-Null Count
Valid values. Ex: Activity 2.3. Relevance: Data cleaning.
Panel
3D structure. Ex: Deprecated. Relevance: Historical.
Tabular Format
Rows/columns. Ex: DataFrame. Relevance: Real-world data.
Numeric Array
NumPy focus. Ex: np.arange. Relevance: Calculations.
Figure-8 Landing
Not in chapter; skip.
Tip: Group by structure; examples for recall. Depth: Code debates. Errors: Length mismatch. Interlinks: To NumPy chapter. Advanced: Pandas updates. Real-Life: Data analysis. Graphs: Attribute tables. Coherent: Intro → Creation → Access. For easy learning: Flashcard per term with code.
Text Book Questions & Answers - NCERT Exercises
Based on activities/think-reflect (no explicit questions). Answers point-wise for exams.
Activity-Based Questions
Activity 2.1: Create a series with five famous monuments of India and their States as index.
Answer:
Code: import pandas as pd; monuments = pd.Series(['Taj Mahal', 'Red Fort', 'Qutub Minar', 'Gateway of India', 'Hawa Mahal'], index=['Uttar Pradesh', 'Delhi', 'Delhi', 'Maharashtra', 'Rajasthan'])
Output: Uttar Pradesh Taj Mahal
Activity 2.2: Get NewDelhi as output using positional index from seriesCapCntry.
Answer:
seriesCapCntry[0] # Assuming index order: India first.
Activity 2.3: Count and display only non-null values in series with NaN.
Answer:
Code: import pandas as pd; import numpy as np; s2=pd.Series([12,np.nan,10]); print(s2.count()) # Output: 2
Think and Reflect: While importing Pandas, is it mandatory to use pd as alias? What if other name?
Answer:
Not mandatory; e.g., import pandas as p; p.Series(). But pd is conventional for readability.
Tip: Practice code (creation/access); full marks: Code + output.
Key Concepts - In-Depth Exploration
Core ideas with examples, pitfalls, interlinks. Expanded: All concepts with steps/examples/pitfalls.
Libraries Comparison
Steps: 1. NumPy numeric arrays, 2. Pandas tabular. Ex: Heterogeneous data. Pitfall: Homogeneous assumption. Interlink: Installation. Depth: Built-on relation.
Series Creation
Steps: 1. Scalars/arrays/dicts, 2. Custom index. Ex: pd.Series([1,2], index=['a','b']). Pitfall: Length error. Interlink: Access. Depth: Scalar repetition.
Indexing Types
Steps: 1. Positional [0], 2. Labelled ['Feb']. Ex: series['Mar']=3. Pitfall: Wrong type. Interlink: Slicing. Depth: List access.
Slicing Differences
Steps: 1. Positional excludes end, 2. Labels include. Ex: [1:3] vs ['USA':'France']. Pitfall: Inclusion error. Interlink: Modification. Depth: Reverse [::-1].
Attributes Usage
Steps: 1. Assign name/index.name, 2. Get values/size. Ex: series.name='Capitals'. Pitfall: Empty check. Interlink: Methods. Depth: Property access.
Methods Application
Steps: 1. head(5) preview, 2. tail(3) end. Ex: series.head(). Pitfall: Large data. Interlink: NaN handling. Depth: Analysis start.
NaN Handling
Steps: 1. Include np.nan, 2. Count non-null. Ex: series.count(). Pitfall: Ignore in calcs. Interlink: Attributes. Depth: Missing data.
Installation Process
Steps: 1. pip install pandas, 2. Import as pd. Ex: Command line. Pitfall: No Python. Interlink: Libraries. Depth: Dependencies.
Data Structures
Steps: 1. Series 1D, 2. DataFrame 2D. Ex: Efficient ops. Pitfall: Confuse dims. Interlink: NumPy. Depth: Visualization link.
Modification
Steps: 1. Slicing assign, 2. Update values. Ex: series[1:3]=50. Pitfall: Label exclusion. Interlink: Slicing. Depth: Bulk updates.
Advanced: Pandas 2.0, large data. Pitfalls: Errors. Interlinks: To DataFrame. Real: Analysis apps. Depth: 10 concepts details. Examples: Code. Graphs: Output tables. Errors: ValueError. Tips: Code evidence; compare (Pandas/NumPy).
Historical Development - Detailed Guide
Evolution of Pandas/NumPy; expanded with points; links to creators/versions. Added global milestones.
NumPy Origins (2005+)
Travis Oliphant 2006: Unified Numeric/Numarray. Multidimensional arrays for science. Depth: From MATLAB inspiration.
Pandas Creation (2008)
Wes McKinney at AQR: For financial data. Panel Data name origin. Depth: Open-source 2009.
Key Milestones
2010: DataFrame enhancements. 2023: Pandas 2.0 (PyArrow, nullables). 2025: AI integration hints. Depth: From finance to general use.
Matplotlib (2003)
John Hunter: MATLAB-like plots. Built on NumPy. Depth: Visualization evolution.
Modern Updates
Pandas 2.0: Faster with Arrow. Integration: Jupyter/PyData. Depth: Efficiency for big data.
Global Impact
Data science standard. Community contributions. Depth: Python ecosystem.
Tip: Link to timelines. Depth: Creator impacts. Examples: Versions. Graphs: Year chronology. Advanced: Future trends. Easy: Bullets milestones.
Technique Examples - From Text with Simple Explanations
Expanded with evidence, interpretations; focus on application, analysis. Added creation/access/slicing breakdowns.
Example 1: Series from Scalars
Simple Explanation: Basic creation.
import pandas as pd
series1 = pd.Series([10,20,30])
print(series1)Output: 0 10\n1 20\n2 30\ndtype: int64
Example 2: Custom Index Series
Simple Explanation: Labeled data.
series2 = pd.Series(["Kavi","Shyam","Ravi"], index=[3,5,1])
print(series2)Output: 3 Kavi\n5 Shyam\n1 Ravi\ndtype: object
Example 3: From NumPy Array
Simple Explanation: Array conversion.
import numpy as np
array1 = np.array([1,2,3,4])
series3 = pd.Series(array1)
print(series3)Output: 0 1\n1 2\n2 3\n3 4\ndtype: int32
Example 4: Positional Indexing
Simple Explanation: Integer access.
seriesNum = pd.Series([10,20,30])
print(seriesNum[2]) # 30
Example 5: Slicing Modification
Simple Explanation: Update subset.
seriesAlph = pd.Series(np.arange(10,16,1), index=['a','b','c','d','e','f'])
seriesAlph[1:3] = 50
print(seriesAlph)Output: a 10\nb 50\nc 50\n...
Example 6: Attributes Usage
Simple Explanation: Property access.
seriesCapCntry.name = 'Capitals'
print(seriesCapCntry.name) # Capitals
Tip: Run code; troubleshoot (e.g., errors). Added for creation/access.
Interactive Quiz - Master Pandas Series
10 MCQs in full sentences; 80%+ goal. Covers creation, access, attributes, methods.
Start Quiz
Quick Revision Notes & Mnemonics
Concise summaries for subtopics. Tables for scan: Key points, examples, mnemonics. Covers creation, access, attributes.
Subtopic
Key Points
Examples
Mnemonics/Tips
Libraries NumPy: Arrays. Pandas: Tabular. Matplotlib: Plots. Pandas heterogeneous.
NPM (NumPy, Pandas, Matplotlib). Tip: "Numbers Process Meaningfully".
Series Creation Scalars/arrays/dicts. Custom index. pd.Series([10,20]).
SAD (Scalars, Arrays, Dicts). Tip: "Sad without data? Create SAD".
Accessing Positional/labelled. Slicing: Exclude/include. series[1:3].
PLS (Positional, Labelled, Slicing). Tip: "Please Label Slices".
Attributes name/index.name. values/size/empty. series.size=4.
NIVSE (Name, Index, Values, Size, Empty). Tip: "Never Ignore Values So Empty".
Methods head(5).
HT (Head, Tail). Tip: "Head To Tail view".
Overall Tip: Use NPM-SAD-PLS-NIVSE-HT for full scan (5 mins). Flashcards: Front (term), Back (points + mnemonic). Covers 100% chapter – easy for exams!
Key Terms & Processes - All Key
Expanded table 30+ rows; quick ref. Added advanced (e.g., dtype, NaN). Overflow fixed.
Term/Process Description Example Usage
Python Libraries Module collections Pandas/NumPy Tasks simplify NumPy Numeric arrays np.array Homogeneous Pandas Data manipulation pd.Series Tabular Matplotlib Plots/graphs plt.plot Visualization Series 1D labeled pd.Series([1,2]) Any type DataFrame 2D tabular pd.DataFrame Heterogeneous Positional Index 0-based series[0] Default Labelled Index Custom series['a'] Meaningful Slicing Subset extract [1:3] Parts name Series label ='Capitals' ID index.name Index label ='Countries' Context values Value list ['NewDelhi'...] Extract size Count 4 Length empty No elements True/False Check head First n head(5) Preview tail Last n tail(3) End Heterogeneous Mixed types Int/string Pandas Homogeneous Same type All ints NumPy ValueError Mismatch Length error Handle pip install Install cmd pip install pandas Setup import as pd Alias pd.Series Convention ndarray NumPy array np.array Base dtype Data type int64 Info Scalar Single value [10] Basic Dictionary Key:value {'India':'NewDelhi'} Mapped index Reverse Slicing Backward [::-1] Order Modification Update [1:3]=50 Bulk NaN Not a Number np.nan Missing Non-Null Valid count count() Clean Panel 3D (deprecated) Old structure Historical Tabular Format Rows/cols DataFrame Real data Numeric Array NumPy focus np.arange Calcs
Tip: Examples memory; sort category. Easy: Table scan. Added 10 rows depth.
Processes Step-by-Step
Step-by-step breakdowns of core processes. Code descriptions; focus on actionable steps with examples.
Process 1: Installing and Importing Pandas
Step 1: Ensure Python/NumPy installed.
Step 2: Run pip install pandas.
Step 3: Import: import pandas as pd.
Step 4: Test: pd.Series().
Visual: Install → Import → Use.
Process 2: Creating Series from Array
Step 1: Import np/pd.
Step 2: Create np.array([1,2,3]).
Step 3: pd.Series(array, index=['a','b','c']).
Step 4: Check lengths match.
Visual: Array → Series → Index.
Process 3: Accessing and Slicing
Step 1: Create series.
Step 2: Positional: series[1].
Step 3: Slicing: series[1:3].
Step 4: Modify: series['b':'d']=50.
Visual: Create → Access → Slice → Update.
Process 4: Using Attributes
Step 1: series.name='Data'.
Step 2: Print series.values.
Step 3: Check series.size.
Step 4: If series.empty: handle.
Visual: Assign → Get → Check.
Process 5: Handling NaN
Step 1: Include np.nan in series.
Step 2: Use series.count() for non-null.
Step 3: series.dropna() if needed.
Step 4: Analyze remaining.
Visual: Include → Count → Clean.
Process 6: Methods Application
Step 1: Create long series.
Step 2: series.head(5).
Step 3: series.tail(3).
Step 4: Preview data.
Visual: Create → Head → Tail.
Tip: Follow like checklist; apply to code. Easy: Number + code per step.
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Read page 1 free Get the kit Get the kit One payment · instant PDF · ₹159 View details Marks booster + cheatsheet: key concepts in 2 lines each 1857 and National Movement map items Constituent Assembly committees table Gandhiji 1915–1931: Champaran to Salt March Permanent Settlement and the Fifth Report Bombay Deccan: ryotwari and the 1875 riots Page 1 free about 8 min read , Full kit about 55 min read ₹159
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This chapter is part of the CBSE Class 12 Board Examination Board Examination Explore every chapter — summary, notes, extra questions & MCQ quizzes Preparing for CBSE Class 12 Board Examination? Students also sit JEE is set on the Class 11 and 12 Physics, Chemistry and Maths syllabus; students prepare for boards and JEE together.
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The Last Lesson
Start here
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Economics (Class 12) Practice Quiz | CBSE Class 12 Board Examination
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Challenges of Nation Building
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Human Geography: Nature and Scope
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Bricks, Beads and Bones — The Harappan Civilisation
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Inverse Trigonometric Functions
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Journey to the End of the Earth
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The Challenges of Cultural Diversity
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Patterns of Social Inequality and Exclusion
#31
Social Institutions: Continuity and Change
#32
The Demographic Structure of the Indian Society
#33
Introducing Indian Society
#34
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Security in the Contemporary World
#36
International Trade (India)
#37
Transport and Communication (India)
#38
Tertiary and Quaternary Activities
#39
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Kings, Farmers and Towns — Early States and Economies (c. 600 BCE-600 CE)
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Theory of Consumer Behaviour
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Nature and Significance of Management
#45
Dissolution of Partnership Firm
#46
Accounting for Partnership: Basic Concepts
#47
Three Dimensional Geometry
#48
Continuity and Differentiability
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Principles of Inheritance and Variation
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Accountancy (Class 12) Practice Quiz | CBSE Class 12 Board Examination
#58
Electrostatic Potential and Capacitance Fundamentals | CBSE Class 12 Board Examination
#59
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An Elementary School Classroom in a Slum
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Social Influence and Group Processes
#63
Attitude and Social Cognition
#64
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Variations in Psychological Attributes
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Mass Media and Communications
#70
Globalisation and Social Change
#71
Change and Development in Industrial Society
#72
Change and Development in Rural Society
#73
The Constitution and Social Change
#74
The Market as a Social Institution
#75
Recent Developments in Indian Politics
#76
The Crisis of Democratic Order
#77
Challenges to and Restoration of the Congress System
#78
India's External Relations
#79
Politics of Planned Development
#80
Era of One-party Dominance
#81
Environment and Natural Resources
#82
International Organisations
#83
#84
Contemporary Centres of Power
#85
Geographical Perspective on Selected Issues and Problems
#86
Planning and Sustainable Development in the Indian Context
#87
Mineral and Energy Resources
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Land Resources and Agriculture
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Population: Distribution, Density, Growth and Composition
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The World Population: Distribution, Density and Growth
#95
Framing the Constitution — The Beginning of a New Era
#96
Mahatma Gandhi and the Nationalist Movement — Civil Disobedience and Beyond
#97
Rebels and the Raj — The Revolt of 1857 and its Representations
#98
Colonialism and the Countryside — Exploring Official Archives
#99
Peasants, Zamindars and the State — Agrarian Society and the Mughal Empire (c. sixteenth-seventeenth centuries)
#100
An Imperial Capital: Vijayanagara (c. fourteenth to sixteenth century)
#101
Bhakti-Sufi Traditions — Changes in Religious Beliefs and Devotional Texts (c. eighth to eighteenth century)
#102
Through the Eyes of Travellers — Perceptions of Society (c. tenth to seventeenth century)
#103
Thinkers, Beliefs and Buildings — Cultural Developments (c. 600 BCE-600 CE)
#104
Kinship, Caste and Class — Early Societies (c. 600 BCE-600 CE)
#105
Open Economy Macroeconomics
#106
Government Budget and the Economy
#107
Determination of Income and Employment
#108
National Income Accounting
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The Theory of the Firm under Perfect Competition
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Analysis of Financial Statements
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Financial Statements of a Company
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Issue and Redemption of Debentures
#125
Accounting for Share Capital
#126
Reconstitution of a Partnership Firm – Retirement/Death of a Partner
#127
Reconstitution of a Partnership Firm – Admission of a Partner
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Application of Derivatives
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Biodiversity and its Conservation
#136
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Organisms and Populations
#138
Biotechnology and its Applications
#139
Biotechnology: Principles and Processes
#140
Microbes in Human Welfare
#141
#142
#143
Molecular Basis of Inheritance
#144
Sexual Reproduction in Flowering Plants
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#147
Aldehydes, Ketones and Carboxylic Acids
#148
Alcohols, Phenols and Ethers
#149
Haloalkanes and Haloarenes
#150
#151
The d- and f-Block Elements
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#153
Semiconductor Electronics: Materials, Devices and Simple Circuits
#154
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#156
Dual Nature of Radiation and Matter
#157
#158
Ray Optics and Optical Instruments
#159
#160
#161
Electromagnetic Induction
#162
#163
Moving Charges and Magnetism
#164
Electrostatic Potential and Capacitance
#165
Electric Charges and Fields
#166
CBSE Class 12 — Genetics and Evolution
#167
CBSE Class 12 — Matrices and Determinants
#168
CBSE Class 12 — Solutions and Colligative Properties
#169
#170
CBSE Class 12 — Electrostatics and Electric Field
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Humanities Subjects Practice Quiz | CBSE Class 12 Board Examination
More on CBSE Class 12 Board Examination20 guides How to Score Good Marks in Class 12 CBSE: Marks Map by Stream CBSE Class 12 Pre-Board Marks: Do They Count, and What to Do If Low Complete Summary and Solutions for Database Concepts – NCERT Class XII Computer Science, Chapter 8 – File Systems, DBMS, Relational Models, Keys, SQL Introduction, Questions, Answers Complete Summary and Solutions for Consumer Education and Protection – NCERT Human Ecology and Family Sciences, Chapter 12 – Study Summary, Explanation, Questions, Answers Complete Summary and Solutions for Supplementary Chemistry Material – NCERT Class XII Chemistry Part I Supplementary Resources – Additional Examples, Problems, and Explanations Complete Summary and Solutions for Project Based Learning – NCERT Class XII Computer Science, Chapter 13 – Project Planning, Implementation, Presentation, Documentation, and Evaluation Complete Summary and Solutions for Querying and SQL Functions – NCERT Class XII Informatics Practices, Chapter 1 – Explanation, Questions, Answers अंतरा भाग 2 – कक्षा 12 के लिए हिंदी (ऐच्छिक) की पाठ्यपुस्तक story खंड – पाठ-16 अंतरा भाग 2 – कक्षा 12 के लिए हिंदी (ऐच्छिक) की पाठ्यपुस्तक काव्य खंड – तुलसीदास: (क) भरत-राम का प्रेम, (ख) पद अंतरा भाग 2 – कक्षा 12 के लिए हिंदी (ऐच्छिक) की पाठ्यपुस्तक story खंड – पाठ-17 अंतरा भाग 2 – कक्षा 12 के लिए हिंदी (ऐच्छिक) की पाठ्यपुस्तक काव्य खंड – मलिक मुहम्मद जायसी: बारहमासा अंतरा भाग 2 – कक्षा 12 के लिए हिंदी (ऐच्छिक) की पाठ्यपुस्तक story खंड – पाठ-15 अंतरा भाग 2 – कक्षा 12 के लिए हिंदी (ऐच्छिक) की पाठ्यपुस्तक काव्य खंड – विष्णु खरे: (क) सत्य, (ख) एक कम अंतरा भाग 2 – कक्षा 12 के लिए हिंदी (ऐच्छिक) की पाठ्यपुस्तक story खंड – पाठ-11 अंतरा भाग 2 – कक्षा 12 हिंदी (ऐच्छिक) काव्य खंड – जयशंकर प्रसाद: (क) देवसेना का गीत, (ख) कार्नेलिया का गीत – संपूर्ण सारांश, व्याख्या, प्रश्न और उत्तर अंतरा भाग 2 – कक्षा 12 के लिए हिंदी (ऐच्छिक) की पाठ्यपुस्तक काव्य खंड – विद्यापति: पद अंतरा भाग 2 – कक्षा 12 के लिए हिंदी (ऐच्छिक) पाठ्यपुस्तक story खंड – पाठ-12 अंतरा भाग 2 – कक्षा 12 के लिए हिंदी (ऐच्छिक) की पाठ्यपुस्तक काव्य खंड – सूर्यकांत त्रिपाठी ‘निराला’: (क) गीत गाने दो मुझे, (ख) सरोज-स्मृति अंतरा भाग 2 – कक्षा 12 के लिए हिंदी (ऐच्छिक) की पाठ्यपुस्तक story खंड – पाठ-10 अंतरा भाग 2 – कक्षा 12 के लिए हिंदी (ऐच्छिक) की पाठ्यपुस्तक काव्य खंड – कुकुआन: दोह Notebook backup + AI Save your notes. Revise with AI. Learn more → Share this article
