Complete Summary and Solutions for Introduction to NumPy – NCERT Class XI Informatics Practices, Chapter 6 – Explanation, Questions, Answers Detailed summary and explanation of Chapter 6 'Introduction to NumPy' from the NCERT Informatics Practices textbook for Class XI, covering the significance of NumPy in numerical computations, creation of arrays, array indexing and slicing, array operations, broadcasting, universal functions (ufuncs), and practical applications in data handling. Includes NCERT questions, answers, and programming exercises. Updated: 8 months ago
Categories: NCERT, Class XI, Informatics Practices, Chapter 6, Python Libraries, NumPy, Numerical Computing, Summary, Questions, Answers, Explanation
Tags: NumPy, Informatics Practices, NCERT, Class 11, Python, Arrays, Broadcasting, Ufuncs, Numerical Computing, Summary, Explanation, Questions, Answers, Chapter 6
Introduction to NumPy - Class 11 Informatics Practices Chapter 6 Ultimate Study Guide 2025
Full Chapter Summary & Detailed Notes
Key Definitions & Terms
Text Book Questions & Answers
Key Concepts
Historical Perspectives
Solved Examples
Interactive Quiz (10 Q)
Quick Revision Notes & Mnemonics
Key Terms & Processes
Processes Step-by-Step
Full Chapter Summary & Detailed Notes - Introduction to NumPy Class 11 NCERT
Overview & Key Concepts
Chapter Goal : Understand NumPy for numerical computing; focus on arrays, operations, efficiency. Exam Focus: Creation, attributes, indexing, arithmetic; 2025 Updates: Integration with ML libs. Fun Fact: Carly Fiorina quote on data insight. Core Idea: Multidimensional arrays speed up processing. Real-World: Data analysis in Python.Wider Scope : From lists to ndarray; sources: Examples (array1-9), tables (list vs array), think/reflect (zeros/ones use). Expanded: Vectorization for performance.Expanded Content : Include broadcasting, ufuncs; point-wise; add 2025 relevance like NumPy in AI.
Introduction to NumPy
Definition : Numerical Python package for scientific computing; uses ndarray for fast data processing.Installation : pip install numpy.Features : Multidimensional arrays, functions for integration (C/C++); speeds up analysis.Example : Import as import numpy as np.Expanded : Evidence: Contiguous memory; debates: vs Pandas; real: Core for SciPy.
Conceptual Diagram: Chapter Structure (In-Text Box)
Bullets: Intro → Array → NumPy Array → Indexing → Operations → Concat/Reshape/Split → Stats → Load/Save. Visualizes from basics to advanced array handling.
Why This Guide Stands Out
Comprehensive: All topics point-wise, code snippets; 2025 with vectorization tips, analyzed for efficiency.
Array Basics
Definition : Ordered collection of same-type elements; contiguous memory for speed.Characteristics : Zero-based indexing; e.g., [10,9,99,71,90] indices 0-4.Think & Reflect : Contiguous vs non-contiguous allocation.Expanded : Evidence: Faster ops; real: Similar to lists but efficient.
NumPy Array (ndarray)
Differences from List (Table 6.1) : Same type, contiguous, element-wise ops, less memory, NumPy lib.Creation from List : np.array([10,20,30]); promotes to common dtype (e.g., strings).1D Array : Single row; e.g., mixed types → string dtype (<U32).2D Array : Nested lists; e.g., [[2.4,3],[4.91,7],[0,-1]] promotes ints to float.Attributes :
ndim : Dimensions (1 for 1D, 2 for 2D).shape : (rows,cols) e.g., (3,).size : Total elements (product of shape).dtype : Element type (int32, float64, <U32).itemsize : Bytes per element (4 for int32, 8 for float64).
Other Creation : dtype=float; zeros((3,4)); ones((3,2)); arange(6) or arange(-2,24,4).Think & Reflect : Zeros/ones for initialization.Expanded : Evidence: Promotion rules; real: Shape for reshaping.
Indexing and Slicing
Indexing : [i,j] for 2D (0-based); e.g., marks[0,2]=56; bounds error for out-of-range.Slicing : [start:end] excludes end; 1D: array8[3:5]=[10,14]; reverse [: : -1]. 2D: array9[0:3,2]=[10,40,4]; all rows [:,2].Example : Marks table (4 students, 3 subjects).Expanded : Evidence: Axis-0 rows, axis-1 cols; real: Data extraction.
Operations on Arrays
Arithmetic (Element-wise) : + - * / % **; same shape required; e.g., array1 + array2.Matrix Multiplication : @ operator.Transpose : Rows to cols; array3.T.Expanded : Evidence: Fast vectorized; real: Broadcasting in advanced.
Exam Activities
Create arrays (Ex 6.1-6.8); slice marks; ops on matrices.
Summary Key Points
NumPy: ndarray for efficient numerical data; attributes (ndim/shape/size/dtype/itemsize); creation (array/zeros/ones/arange); indexing/slicing; ops (+-*/@T).
Impact: Faster than lists; challenges: Dtype promotion.
Project & Group Ideas
Group: 2D array for student marks analysis; individual: Stats on loaded data.
Debate: NumPy vs lists efficiency.
Ethical role-play: Data privacy in arrays.
Key Definitions & Terms - Complete Glossary
All terms from chapter; detailed with examples, relevance. Expanded: 30+ terms grouped by subtopic; added advanced like "broadcasting", "ufunc" for depth/easy flashcards. Table overflow fixed with word-break.
NumPy
Numerical Python package. Ex: Scientific computing. Relevance: Array tools.
ndarray
N-dimensional array. Ex: array([10,20,30]). Relevance: Core object.
Contiguous Memory
Sequential storage. Ex: Fast access. Relevance: Speed vs lists.
Zero-Based Indexing
Starts at 0. Ex: [0] first element. Relevance: Python standard.
dtype
Data type. Ex: int32, float64. Relevance: Uniform elements.
ndim
Number of dimensions. Ex: 1 for 1D. Relevance: Axes/rank.
shape
Size per dimension. Ex: (3,2). Relevance: Rows/cols.
size
Total elements. Ex: 6 for (3,2). Relevance: Product.
itemsize
Bytes per element. Ex: 4 for int32. Relevance: Memory.
zeros
Array of zeros. Ex: np.zeros((3,4)). Relevance: Initialization.
ones
Array of ones. Ex: np.ones((3,2)). Relevance: Defaults.
arange
Range array. Ex: np.arange(-2,24,4). Relevance: Sequences.
Indexing
Access by [i,j]. Ex: array[0,2]. Relevance: Elements.
Slicing
Subset [start:end]. Excludes end. Relevance: Parts.
Element-wise Ops
Per-element arithmetic. Ex: array + 3. Relevance: Vectorized.
Transpose
Rows to cols. Ex: array.T. Relevance: Matrix ops.
Broadcasting
Advanced: Shape-compatible ops. Ex: array + scalar. Relevance: 2025 efficiency.
ufunc
Universal functions. Ex: np.add. Relevance: Fast ops.
1D Array
Single row. Ex: np.array([1,2,3]). Relevance: Vectors.
2D Array
Matrix. Ex: Nested lists. Relevance: Tables.
Promotion
Dtype upgrade. Ex: Int to float. Relevance: Mixed inputs.
Axis-0
Rows. Ex: Vertical. Relevance: Dimensions.
Axis-1
Columns. Ex: Horizontal. Relevance: 2D ops.
Rank
Number of axes. Ex: ndim. Relevance: Complexity.
Nested List
List of lists. Ex: For 2D. Relevance: Creation.
IndexError
Out of bounds. Ex: [0,4] for size 3. Relevance: Bounds check.
Matrix Mult
@ operator. Ex: array1 @ array2. Relevance: Linear algebra.
Modulo
% remainder. Ex: array2 % array1. Relevance: Arithmetic.
Exponentiation
** power. Ex: array1 ** 3. Relevance: Math ops.
U32
Unicode-32. Ex: String dtype. Relevance: Text arrays.
Vectorization
Array ops without loops. Ex: Element-wise. Relevance: Speed.
REPL
Interactive Python. Ex: >>> prompts. Relevance: Testing.
Tip: Group by topic; examples for recall. Depth: Debates (e.g., dtype overhead). Errors: Forget brackets. Interlinks: To Ch5 Python. Advanced: Broadcasting. Real-Life: Data viz. Graphs: Memory comparison. Coherent: Evidence → Interpretation. For easy learning: Flashcard per term with code.
Text Book Questions & Answers - NCERT Exercises
Based on chapter examples/activities (inferred from content). Answers point-wise for exams; includes think/reflect.
Short Answer Questions
1. What is NumPy? How to install it?
Answer:
Package for numerical computing with arrays.
Install: pip install numpy.
2. Differentiate between list and NumPy array.
Answer:
List: Mixed types, non-contiguous, no element-wise ops.
Array: Same type, contiguous, supports ops, less memory.
3. What are attributes of ndarray? Explain ndim and shape.
Answer:
Attributes: ndim, shape, size, dtype, itemsize.
ndim: Dimensions (1/2).
shape: (rows,cols) e.g., (3,2).
Medium Answer Questions
4. How to create 1D and 2D arrays? Give examples.
Answer:
1D: np.array([10,20,30]).
2D: np.array([[2.4,3],[4.91,7]]); promotes to float.
5. Explain indexing and slicing for 2D arrays with example.
Answer:
Indexing: [i,j] e.g., [0,2].
Slicing: [1:3,0:2] for subarray; [:,2] all rows col 2.
Long Answer Questions
6. Describe arithmetic operations on arrays. Why same shape required?
Answer:
Ops: + - * / % ** @; element-wise.
Same shape: Pairwise application; e.g., array1 + array2.
7. What is transpose? Give code example.
Answer:
Swaps rows/cols: array.T.
Ex: 2x3 → 3x2.
8. When to use zeros/ones/arange? Examples.
Answer:
zeros: Init matrices; np.zeros((3,4)).
ones: Defaults; np.ones((3,2)).
arange: Sequences; np.arange(6).
9. What error occurs on out-of-bounds index? Example.
Answer:
IndexError: e.g., marks[0,4] for 3 cols.
10. Explain dtype promotion with example.
Answer:
Mixed → common type; e.g., [5,-7.4,'a'] → <U32 strings.
11. For a marks array (4x3), how to access Prasad's English mark?
Answer:
marks[3,1] = 72 (0-based).
12. How slicing excludes end index? Example.
Answer:
[3:5] = indices 3,4; e.g., [10,14].
13. What is itemsize for float64 array?
14. Differentiate arange from range.
Answer:
arange: Array output; range: Iterator.
15. Match: ndim → 1D value; shape → (3,); etc.
Answer:
ndim:1; shape:(3,); size:3; dtype:int32.
Tip: Practice code (Q4); matching (Q15). Full marks: Point-wise, code snippets.
Key Concepts - In-Depth Exploration
Core ideas with examples, pitfalls, interlinks. Expanded: All concepts with steps/examples/pitfalls for easy learning. Depth: Debates, analysis. Table overflow fixed.
Array Creation
Steps: 1. Import np, 2. np.array(list). Ex: [10,20]. Pitfall: No brackets. Interlink: Dtype. Depth: Promotion.
Attributes Query
Steps: 1. array.ndim, 2. array.shape. Ex: (3,2). Pitfall: Ignore rank. Interlink: Size=product. Depth: Memory calc.
Indexing Access
Steps: 1. [i] 1D, [i,j] 2D. Ex: [0,2]. Pitfall: Bounds. Interlink: Slicing. Depth: Axis.
Slicing Subset
Steps: 1. [start:end], 2. Excludes end. Ex: [3:5]. Pitfall: Negative step. Interlink: Views. Depth: Efficient.
Element-wise Ops
Steps: 1. array + scalar/array. Ex: /3. Pitfall: Shape mismatch. Interlink: Broadcasting. Depth: Vectorized speed.
Transpose Swap
Steps: 1. array.T. Ex: Rows to cols. Pitfall: In-place no. Interlink: Matrix mult. Depth: Linear alg.
Dtype Uniform
Steps: 1. Specify dtype=float. Ex: Promotion. Pitfall: Memory waste. Interlink: Itemsize. Depth: Efficiency.
Contiguous Storage
Steps: 1. Allocate sequential. Ex: Fast ops. Pitfall: Fragmented slow. Interlink: Lists. Depth: Cache-friendly.
Initialization Zeros/Ones
Steps: 1. np.zeros(shape). Ex: Matrices. Pitfall: Wrong shape. Interlink: Arange. Depth: Defaults.
Sequence Arange
Steps: 1. np.arange(start,stop,step). Ex: -2 to 22 step 4. Pitfall: Float steps. Interlink: Linspace advanced. Depth: Ranges.
Matrix Mult
Steps: 1. array1 @ array2. Ex: Dot product. Pitfall: Shape incompatible. Interlink: Transpose. Depth: Algebra.
Promotion Rules
Steps: 1. To common supertype. Ex: Int+str=unicode. Pitfall: Unexpected dtype. Interlink: Creation. Depth: Safety.
Memory Efficiency
Steps: 1. No per-element type. Ex: Arrays < lists. Pitfall: Large strings. Interlink: Itemsize. Depth: Scalability.
Advanced: Broadcasting checklists, shape checks. Pitfalls: IndexError. Interlinks: To Ch7 Pandas. Real: Array in ML. Depth: 12 concepts details. Examples: Real code. Graphs: Time comparison. Errors: Shape mix. Tips: Steps evidence; compare tables (list vs array).
Historical Perspectives - Detailed Guide
Evolution of NumPy; expanded with points; links to pioneers/debates. Added origins, modern uses.
NumPy Origins (2006)
From Numeric/Numeric Python. Travis Oliphant unified. Depth: Array standardization.
Python Arrays (1990s)
Early lists; inefficient for numerics. NumPy boost: C integration. Depth: From scripting to scientific.
SciPy Ecosystem (2000s)
NumPy base for SciPy/Matplotlib. Vectorization key. Depth: Open-source growth.
Modern NumPy (2010s+)
1.20+ masked arrays. 2025: JIT compilation. Depth: ML integration (TensorFlow).
Array Concept (1960s)
Fortran arrays; influenced Python. Contiguous from hardware. Depth: Numerical roots.
CBSE Inclusion (2010s)
Class 11 IP: Data handling. Builds to advanced computing. Depth: Education evolution.
Tip: Link to milestones. Depth: Reflexive efficiency. Examples: Oliphant. Graphs: Adoption timeline. Advanced: Post-2025 Numba. Easy: Bullets impacts.
Solved Examples - From Text with Simple Explanations
Expanded with evidence, code; focus on creation, ops, analysis. Added attribute calc, slicing.
Example 1: 2D Array Creation (Ex 6.1)
Simple Explanation: Nested lists to matrix.
Step 1: array3 = np.array([[2.4,3],[4.91,7],[0,-1]]).
Step 2: Promotes to float64.
Output: [[2.4,3],[4.91,7],[0,-1]].
Simple Way: List of rows → array.
Example 2: Attributes (Ex 6.3)
Simple Explanation: Query shape.
Step 1: array1.shape → (3,).
Step 2: array3.shape → (3,2).
Step 3: size = 3*2=6.
Simple Way: Dim check.
Example 3: Zeros/Ones (Creation)
Simple Explanation: Init matrices.
Step 1: np.zeros((3,4)) all 0.0.
Step 2: np.ones((3,2)) all 1.0.
Step 3: dtype=float default.
Simple Way: Fill defaults.
Example 4: Arange Sequence
Simple Explanation: Generate range.
Step 1: np.arange(6) → [0,1,2,3,4,5].
Step 2: np.arange(-2,24,4) → [-2,2,6,10,14,18,22].
Step 3: Stop exclusive.
Simple Way: Start-stop-step array.
Example 5: Slicing 2D (Ex 6.7)
Simple Explanation: Extract column.
Step 1: array9[:,2] → [10,40,4].
Step 2: [1:3,0:2] submatrix.
Step 3: End excluded.
Simple Way: Rows:cols slice.
Example 6: Arithmetic Ops
Simple Explanation: Element-wise add.
Step 1: array1 + array2 → pairwise sum.
Step 2: array1 @ array2 matrix mult.
Step 3: Same shape req.
Simple Way: Op on arrays = vector op.
Tip: Practice self-run; troubleshoot (e.g., shape errors). Added for attributes, ops.
Interactive Quiz - Master NumPy
10 MCQs in full sentences; 80%+ goal. Covers creation, attributes, ops, etc.
Start Quiz
Quick Revision Notes & Mnemonics
Concise summaries for subtopics. Tables for quick scan: Key points, examples, mnemonics. Covers creation, attributes, ops. Bold terms; short phrases. Overflow fixed.
Subtopic
Key Points
Examples
Mnemonics/Tips
Basics & Creation NumPy : Import np; np.array(list).1D/2D : Single/nested; dtype promote.zeros/ones/arange: Init/seq. np.array([10,20]); [[2.4,3]].ZAO (Zeros, Arange, Ones). Tip: "Zero Arrays Often" – Init tools.
Attributes ndim/shape : Dims/(rows,cols).size/dtype : Total/type.itemsize : Bytes.ndim=2; shape=(3,2); dtype=float64.
NSSD I (Ndim, Shape, Size, Dtype, Itemsize). Tip: "NumPy Shapes Size Data Items" – Query chain.
Indexing/Slicing Index : [i,j] 0-based.Slice : [start:end] exclude end; [:,col].Reverse: [:: -1]. [0,2]=56; [3:5]=[10,14].
ISR (Index, Slice, Reverse). Tip: "Index Slices Rows" – Access tricks.
Operations Arithmetic : Element-wise +-*/%**.Matrix : @ mult.Transpose : .T swap.array1 + array2; array1 @ array2.
AMT (Arithmetic, Matrix, Transpose). Tip: "Add Multiply Turn" – Ops flow.
Overall Tip: Use ZAO-NSSD I-ISR-AMT for scan (5 mins). Flashcards: Front (term), Back (code + mnemonic). Print table. Covers 100% – easy exams!
Key Terms & Processes - All Key
Expanded table 30+ rows; quick ref. Added advanced (e.g., Broadcasting, Vectorization). Overflow fixed.
Term/Process Description Example Usage
NumPy Numerical Python package import numpy as np Computing ndarray N-dim array object np.array([1,2]) Core Contiguous Sequential memory Fast ops Speed Zero-Based Index from 0 [0] first Access dtype Element data type int32 Uniform ndim Dimensions count 2 for matrix Rank shape Size tuple (3,2) Structure size Total elements 6 Count itemsize Bytes per elem 8 float64 Memory zeros Zero-filled array np.zeros((3,4)) Init ones One-filled array np.ones((3,2)) Default arange Range generator np.arange(6) Seq Indexing Position access [0,2] Element Slicing Range subset [3:5] Part Element-wise Per-elem op array + 3 Vector Transpose Row-col swap array.T Matrix Broadcasting Shape compat ops array + scalar Advanced ufunc Universal function np.add Fast 1D Array Vector row [1,2,3] Simple 2D Array Matrix table [[1,2],[3,4]] Data Promotion Type upgrade Int to float Mixed Axis-0 Row dimension Vertical 2D Axis-1 Col dimension Horizontal 2D Rank Axes number ndim Complexity Nested List List of lists For 2D Creation IndexError Bounds exceed [0,4] size3 Check Matrix Mult Dot product @ op Algebra Modulo Remainder div % Math Exponent Power raise **3 Math U32 Unicode string <U32 Text Vectorization No-loop ops Element-wise Perf REPL Interactive shell >>> Test Promotion Rule Common supertype Str dominates Safety Memory Alloc Contiguous blocks Itemsize * size Efficiency
Tip: Examples memory; sort subtopic. Easy: Table scan. Added 10 rows depth.
Processes Step-by-Step
Step-by-step breakdowns of core processes. Visual descriptions; no diagrams, actionable steps with code. Overflow fixed.
Process 1: Array Creation from List
Step 1: Import import numpy as np.
Step 2: array = np.array([10,20,30]).
Step 3: Check dtype/shape.
Step 4: Promote if mixed.
Step 5: Use in ops.
Visual: List → np.array → ndarray.
Process 2: 2D Array Build
Step 1: Nested list [[1,2],[3,4]].
Step 2: np.array(nested).
Step 3: Specify dtype=float if needed.
Step 4: Verify shape (2,2).
Step 5: Index [0,1]=2.
Visual: Rows list → Matrix.
Process 3: Indexing/Slicing
Step 1: array[0] first elem.
Step 2: [1:3] middle slice.
Step 3: 2D [0:2,1] col subset.
Step 4: Reverse [::-1].
Step 5: Avoid bounds error.
Visual: Array → Slice view.
Process 4: Arithmetic Element-wise
Step 1: Ensure same shape.
Step 2: array1 + array2.
Step 3: /3 divide all.
Step 4: @ for matrix.
Step 5: Check result dtype.
Visual: Pairwise → New array.
Process 5: Transpose Operation
Step 1: Create 2D array.
Step 2: array.T or .transpose().
Step 3: Shape swaps (m,n)→(n,m).
Step 4: Use in mult.
Step 5: Verify elements.
Visual: Rows become cols.
Process 6: Attribute Calculation
Step 1: array.ndim.
Step 2: array.shape.
Step 3: size = np.prod(shape).
Step 4: itemsize * size = memory.
Step 5: dtype.name.
Visual: Query → Info tuple.
Tip: Follow like recipe; apply to ex (6.1-6.8). Easy: Number + code per step.
As an Amazon Associate, ProSyllabus earns from qualifying purchases. Prices shown are subject to change.
This chapter is part of the CBSE Class 11 Annual Assessment Board Examination Explore every chapter — NCERT notes, important questions & MCQ quizzes Playing as guest — sign in so your rank, XP and attempts aren't lost #1
Constitution: Why and How?
6.8/10 avg score
9/10 best
leaderboard ranks ›#2
What is Psychology?
8.8/10 avg score
10/10 best
leaderboard ranks ›#3
India – Location
6/10 avg score
10/10 best
leaderboard ranks ›#4
Nomadic Empires
8/10 avg score
9/10 best
leaderboard ranks ›#5
Animal Kingdom
2.3/10 avg score
5/10 best
leaderboard ranks ›#6
The Living World
6/10 avg score
9/10 best
leaderboard ranks ›#7
Mother's Day
10/10 avg score
10/10 best
leaderboard ranks ›#8
A Photograph
8/10 avg score
9/10 best
leaderboard ranks ›#9
Methods of Enquiry in Psychology
9.5/10 avg score
10/10 best
leaderboard ranks ›#10
Election and Representation
7.5/10 avg score
8/10 best
leaderboard ranks ›#11
Political Theory: An Introduction
7/10 avg score
10/10 best
leaderboard ranks ›#12
An Empire Across Three Continents
8.5/10 avg score
9/10 best
leaderboard ranks ›#13
Plant Kingdom
3.5/10 avg score
4/10 best
leaderboard ranks ›#14
Biological Classification
8.5/10 avg score
9/10 best
leaderboard ranks ›#15
Motion in a Straight Line
2.5/10 avg score
4/10 best
leaderboard ranks ›#16
Accountancy (Class 11) Practice Quiz | CBSE Class 11 Annual Assessment
2/10 avg score
4/10 best
leaderboard ranks ›#17
Sets and Venn Operations Fundamentals — Free CBSE Class 11 Annual Assessment Quiz
2/10 avg score
3/10 best
leaderboard ranks ›#18
The Summer of the Beautiful White Horse
9/10 avg score
9/10 best
leaderboard ranks ›#19
The Laburnum Top
10/10 avg score
10/10 best
leaderboard ranks ›#20
Discovering Tut: the Saga Continues
10/10 avg score
10/10 best
leaderboard ranks ›#21
We're Not Afraid to Die... if We Can All Be Together
10/10 avg score
10/10 best
leaderboard ranks ›#22
The Portrait of a Lady
7/10 avg score
7/10 best
leaderboard ranks ›#23
Learning
8/10 avg score
8/10 best
leaderboard ranks ›#24
Federalism
10/10 avg score
10/10 best
leaderboard ranks ›#25
Rights in the Indian Constitution
7/10 avg score
7/10 best
leaderboard ranks ›#26
Social Justice
10/10 avg score
10/10 best
leaderboard ranks ›#27
Freedom
10/10 avg score
10/10 best
leaderboard ranks ›#28
Water in the Atmosphere
10/10 avg score
10/10 best
leaderboard ranks ›#29
Changing Cultural Traditions
10/10 avg score
10/10 best
leaderboard ranks ›#30
Writing and City Life
9/10 avg score
9/10 best
leaderboard ranks ›#31
Indian Economy 1950-1990
9/10 avg score
9/10 best
leaderboard ranks ›#32
Introduction
5/10 avg score
5/10 best
leaderboard ranks ›#33
Private, Public and Global Enterprises
9/10 avg score
9/10 best
leaderboard ranks ›#34
Introduction to Accounting
9/10 avg score
9/10 best
leaderboard ranks ›#35
Cell: The Unit of Life
10/10 avg score
10/10 best
leaderboard ranks ›#36
Anatomy of Flowering Plants
4/10 avg score
4/10 best
leaderboard ranks ›#37
Organic Chemistry – Some Basic Principles and Techniques
3/10 avg score
3/10 best
leaderboard ranks ›#39
Motion in a Plane
5/10 avg score
5/10 best
leaderboard ranks ›#48
The Ailing Planet: the Green Movement's Role
#53
Sensory, Attentional and Perceptual Processes
#56
Introducing Western Sociologists
#58
Social Change and Social Order in Rural and Urban Society
#59
Social Structure, Stratification and Social Processes in Society
#60
Doing Sociology: Research Methods
#61
Culture and Socialisation
#62
Understanding Social Institutions
#63
Terms, Concepts and Their Use in Sociology
#65
The Philosophy of the Constitution
#66
Constitution as a Living Document
#76
Natural Hazards and Disasters
#80
Structure and Physiography
#81
Biodiversity and Conservation
#84
World Climate and Climate Change
#85
Atmospheric Circulation and Weather Systems
#86
Solar Radiation, Heat Balance and Temperature
#87
Composition and Structure of Atmosphere
#88
Landforms and their Evolution
#90
Distribution of Oceans and Continents
#92
The Origin and Evolution of the Earth
#93
Geography as a Discipline
#95
Displacing Indigenous Peoples
#97
Comparative Development Experiences of India and its Neighbours
#98
Environment and Sustainable Development
#99
Employment: Growth, Informalisation and Other Issues
#101
Human Capital Formation in India
#102
Liberalisation, Privatisation and Globalisation: An Appraisal
#103
Indian Economy on the Eve of Independence
#107
Measures of Central Tendency
#113
MSME and Business Entrepreneurship
#114
Sources of Business Finance
#116
Social Responsibilities of Business and Business Ethics
#117
Emerging Modes of Business
#119
Forms of Business Organisation
#120
Business, Trade and Commerce
#121
Financial Statements - II
#123
Depreciation, Provisions and Reserves
#124
Trial Balance and Rectification of Errors
#125
Bank Reconciliation Statement
#126
Recording of Transactions - II
#127
Recording of Transactions - I
#128
Theory Base of Accounting
#132
Introduction to Three Dimensional Geometry
#137
Permutations and Combinations
#139
Complex Numbers and Quadratic Equations
#142
Chemical Coordination and Integration
#143
Neural Control and Coordination
#145
Excretory Products and their Elimination
#146
Body Fluids and Circulation
#147
Breathing and Exchange of Gases
#148
Plant Growth and Development
#150
Photosynthesis in Higher Plants
#151
Cell Cycle and Cell Division
#153
Structural Organisation in Animals
#154
Morphology of Flowering Plants
#157
Chemical Bonding and Molecular Structure
#158
Classification of Elements and Periodicity in Properties
#159
Some Basic Concepts of Chemistry
#164
Thermal Properties of Matter
#165
Mechanical Properties of Fluids
#166
Mechanical Properties of Solids
#168
Systems of Particles and Rotational Motion
#173
Business Studies (Class 11) Practice Quiz | CBSE Class 11 Annual Assessment
#174
Economics (Class 11) Practice Quiz | CBSE Class 11 Annual Assessment
#175
Humanities Subjects Practice Quiz | CBSE Class 11 Annual Assessment
#176
Motion in a Straight Line Practice Quiz | CBSE Class 11 Annual Assessment
#177
Thermodynamic Processes and Laws Advanced Challenge | CBSE Class 11 Annual Assessment
Group Discussions No forum posts available.
Easily Share with Your Tribe
