Here is the super-brief, exam-ready, topic-by-topic Module-4 summary, exactly in the same order as the picture, with PDF topics (✔) and web topics (🌐) merged smoothly.
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MODULE–4: MODULES & PACKAGES (SUPER BRIEF NOTES)
1. Modules and Packages
1.1 Introduction ✔
A module = Python file with code.
Helps organize large programs into smaller parts.
1.2 Importing Modules ✔
import module
from module import name
from module import *
1.3 Module & Member Aliasing ✔
import module as m
from module import func as f
1.4 Built-in Modules ✔
Examples: math, random, statistics, sys.
Used for ready-made functions.
1.5 User-Defined Functions 🌐
Created using def.
Reusable code block.
1.6 Arguments 🌐
Positional, Keyword, Default.
*args (many values), **kwargs (many keywords).
1.7 Communication With Environment (Scopes) 🌐
LEGB rule: Local → Enclosing → Global → Built-in.
global and nonlocal allow modifying parent scopes.
1.8 Returning Results 🌐
return sends value back.
No return → None.
1.9 Scopes 🌐
Local, Global, Enclosing, Built-in.
1.10 Recursion 🌐
Function calling itself.
Needs base case + recursive step.
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2. Python Modules
2.1 NumPy ✔
Library for fast array & numerical operations.
2.2 Pandas 🌐
Data analysis library (Series & DataFrames).
2.3 SciPy 🌐
Scientific computing (optimization, integration).
2.4 Django 🌐
Web development framework.
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3. Working with NumPy
3.1 Introduction ✔
Numerical computing library; fast arrays.
3.2 Installing ✔
pip install numpy
3.3 Arrays vs Lists ✔
Arrays → faster, homogeneous, continuous memory.
3.4 Data Types ✔
Each array has dtype; upcasting occurs.
3.5 Creating Arrays ✔
np.array(), np.arange(), np.empty()
3.6 Arithmetic ✔
Add, subtract, multiply, divide element-wise.
3.7 Indexing ✔
arr[i], arr[row, col]
3.8 Slicing ✔
Extract parts: arr[1:3], arr[0:2,1:3]
3.9 Copy vs View ✔
copy() = separate
view() = shares data
3.10 Shape ✔
arr.shape → dimensions.
3.11 Reshape ✔
Change dimensions: reshape(r,c)
3.12 Joins ✔
np.concatenate()
3.13 Subsets ✔
Access selected rows/cols.
3.14 Split ✔
np.array_split(arr, n)
3.15 Search ✔
np.where(), np.searchsorted()
3.16 Sort ✔
np.sort()
3.17 Filter ✔
Boolean filtering: arr[condition]
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✅ SUPER-BRIEF, CLEAN, EXAM-READY MODULE–4 IS DONE.
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