🐍 Python Learnings

A structured, self-paced Python learning repository — from absolute basics to data science and beyond.

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📚 Table of Contents

#	Chapter	Topics Covered
01	Intro To Python - 1	Setup, syntax, variables, data types, input/output
02	Intro To Python - 2	Type casting, comments, keywords, naming conventions
03	Operators	Arithmetic, comparison, logical, bitwise, assignment operators
04	Strings	String methods, slicing, formatting, indexing
05	Data Structures - 1	Lists, Tuples — creation, indexing, methods
06	Data Structures - 2	Sets, Dictionaries — operations and methods
07	Conditional STMT	if, elif, else, nested conditions
08	Looping Stmt	for loop, while loop, nested loops
09	Control Transfer Stmts	break, continue, pass
10	Functions	def, return, *args, **kwargs, lambda, recursion
11	Exception Handling	try, except, finally, raise, custom exceptions
12	Modules	import, built-in modules, custom modules, packages
13	File Handling	Read, write, append files; context managers
14	NumPy - I	Arrays, array operations, indexing, slicing
15	NumPy - II	Mathematical ops, broadcasting, reshaping, aggregations
16	Pandas - I	Series, DataFrames, reading CSV/Excel, basic operations
17	Pandas - II	Filtering, groupby, merging, joining DataFrames
18	Pandas - III	Data cleaning, handling nulls, apply, pivot tables
19	Matplotlib & Seaborn	Line, bar, pie, scatter plots; heatmaps, pairplots
20	OOPs	Classes, objects, inheritance, polymorphism, encapsulation
21	Database Connectivity	MySQL/SQLite connection, CRUD operations with Python
🦠	Case Study — COVID	Real-world data analysis using COVID-19 dataset
📝	Python Test	Practice tests and assessments
📅	Weekly	Weekly exercises and revision tasks

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🛠️ Tools & Libraries Used

Tool / Library	Purpose
Python 3.x	Core language
Jupyter Notebook	Interactive coding environment
NumPy	Numerical computing
Pandas	Data manipulation
Matplotlib	Data visualization
Seaborn	Statistical visualization
SQLite / MySQL	Database connectivity

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🗺️ Expanded Learning Path

🟢 Core Python — Chapters 01 to 13

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📘 Chapter 01 — Intro To Python - 1

• What is Python?
• Python Features (Simple, Interpreted, Open Source, Portable, etc.)
• Python vs Other Languages & Tools (Java, R, Excel, etc.)
• Python Applications — Web, Automation, AI/ML, Data Analytics
• Python's Role in Data Analytics & Data Science
• Installing Python & Setting Up the Environment (Anaconda / pip)
• Introduction to Jupyter Notebook
• Writing & Running Your First Python Program

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📘 Chapter 02 — Intro To Python - 2

• Variables — Declaration, Assignment, Naming Rules
• Python Data Types — int, float, complex, str, bool, NoneType
• type() and isinstance() functions
• Type Casting — int(), float(), str(), bool()
• input() and print() functions
• Python Keywords & Reserved Words
• Comments — Single-line (#) and Multi-line (''')
• Naming Conventions & PEP 8 Basics
• Dynamic Typing in Python

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📘 Chapter 03 — Operators

• Arithmetic Operators — +, -, *, /, //, %, **
• Comparison / Relational Operators — ==, !=, >, <, >=, <=
• Logical Operators — and, or, not
• Assignment Operators — =, +=, -=, *=, /=, //=, **=
• Bitwise Operators — &, |, ^, ~, <<, >>
• Identity Operators — is, is not
• Membership Operators — in, not in
• Operator Precedence & Associativity

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📘 Chapter 04 — Strings

• String Creation & Literals
• String Indexing (Positive & Negative)
• String Slicing — str[start:stop:step]
• String Immutability
• String Methods — upper(), lower(), strip(), lstrip(), rstrip(), replace(), split(), join(), find(), count(), startswith(), endswith()
• String Formatting — f-strings, .format(), % operator
• Escape Characters — \n, \t, \\
• Raw Strings — r"..."
• Multi-line Strings

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📘 Chapter 05 — Data Structures - 1

Lists

• Creating Lists, Nested Lists
• Indexing & Slicing
• List Methods — append(), insert(), remove(), pop(), sort(), reverse(), copy(), extend(), count(), index()
• List Comprehensions
• Mutable Nature of Lists

Tuples

• Creating Tuples, Single-element Tuples
• Indexing & Slicing
• Tuple Methods — count(), index()
• Tuple Unpacking / Packing
• Immutability — Why Tuples over Lists?

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📘 Chapter 06 — Data Structures - 2

Sets

• Creating Sets, Empty Sets
• Set Methods — add(), remove(), discard(), pop(), clear()
• Set Operations — Union (|), Intersection (&), Difference (-), Symmetric Difference (^)
• Frozensets

Dictionaries

• Creating Dictionaries, Nested Dictionaries
• Accessing & Modifying Values
• Dictionary Methods — get(), update(), keys(), values(), items(), pop(), setdefault()
• Dictionary Comprehensions
• Iterating Over Dictionaries

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📘 Chapter 07 — Conditional Statements

• if Statement
• if-else Statement
• if-elif-else Ladder
• Nested if Conditions
• Ternary / Inline Conditional Expressions — x if condition else y
• Truthiness & Falsy Values in Python
• Practical Examples — Grade system, eligibility checks, etc.

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📘 Chapter 08 — Looping Statements

• for Loop — Iterating over Lists, Strings, Tuples, Ranges
• range() Function — start, stop, step
• while Loop — Condition-based Iteration
• Nested Loops
• enumerate() — Loop with Index
• zip() — Iterate over Multiple Iterables
• Looping with Dictionaries

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📘 Chapter 09 — Control Transfer Statements

• break — Exit a Loop Prematurely
• continue — Skip to the Next Iteration
• pass — Placeholder / Empty Block
• else with Loops (for-else / while-else)
• Practical Use Cases and Examples

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📘 Chapter 10 — Functions

• Defining Functions with def
• Function Parameters & Return Values
• Default Arguments
• Keyword Arguments (kwargs)
• Variable-length Arguments — *args and **kwargs
• Lambda (Anonymous) Functions
• Recursive Functions
• Scope — Local, Global, global keyword
• map(), filter(), reduce()
• Docstrings & Function Documentation

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📘 Chapter 11 — Exception Handling

• Types of Errors — Syntax, Runtime, Logical
• try and except Blocks
• Handling Specific Exceptions — ValueError, TypeError, ZeroDivisionError, FileNotFoundError, IndexError, KeyError
• else Block in Exception Handling
• finally Block — Always Executes
• Raising Exceptions with raise
• Custom / User-Defined Exceptions
• Best Practices for Error Handling

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📘 Chapter 12 — Modules

• What is a Module?
• import Statement and from...import
• import ... as Aliasing
• Built-in Modules — math, random, datetime, os, sys, time
• Creating Custom Modules
• __name__ == "__main__" Pattern
• Python Packages & __init__.py
• Installing Third-party Packages with pip

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📘 Chapter 13 — File Handling

• Opening Files — open() function
• File Modes — r, w, a, r+, rb, wb
• Reading Files — read(), readline(), readlines()
• Writing & Appending to Files
• Context Manager — with open(...) as f
• Working with CSV Files using csv module
• File & Directory Operations with os module
• Handling File-related Exceptions

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🔵 Data Science Libraries — Chapters 14 to 19

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📗 Chapter 14 — NumPy - I

• What is NumPy? Why use it over Python Lists?
• Installing & Importing NumPy
• Creating Arrays — np.array(), np.zeros(), np.ones(), np.full(), np.arange(), np.linspace()
• Array Attributes — shape, dtype, ndim, size, itemsize
• 1D, 2D, and 3D Arrays
• Array Indexing & Slicing
• Array Data Types & Type Conversion

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📗 Chapter 15 — NumPy - II

• Element-wise Arithmetic Operations
• Broadcasting — Rules and Examples
• Reshaping & Transposing — reshape(), flatten(), .T
• Stacking & Splitting — hstack(), vstack(), split()
• Mathematical Functions — np.sqrt(), np.exp(), np.log()
• Statistical Functions — np.sum(), np.mean(), np.std(), np.min(), np.max(), np.median()
• Boolean Masking & Fancy Indexing
• Sorting Arrays — np.sort(), np.argsort()
• Random Module — np.random.rand(), np.random.randint(), np.random.seed()

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📗 Chapter 16 — Pandas - I

• What is Pandas? Why use it?
• Series — Creation, Indexing, Operations
• DataFrame — Creation, Structure, Attributes
• Reading Data — pd.read_csv(), pd.read_excel()
• Exploring DataFrames — head(), tail(), info(), describe(), shape, columns, dtypes
• Selecting Columns — Single & Multiple
• Row Selection — loc[] and iloc[]
• Adding & Dropping Columns and Rows
• Sorting — sort_values(), sort_index()

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📗 Chapter 17 — Pandas - II

• Filtering Rows with Conditions
• Multiple Conditions — &, |, ~
• groupby() — Grouping and Aggregation (sum, mean, count, max, min)
• Merging DataFrames — pd.merge() (inner, outer, left, right joins)
• Joining DataFrames — join()
• Concatenating — pd.concat() (axis 0 & 1)
• Pivot Tables — pd.pivot_table()
• Cross Tabulation — pd.crosstab()

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📗 Chapter 18 — Pandas - III

• Identifying Missing Values — isnull(), notnull(), isna()
• Handling Missing Values — dropna(), fillna()
• Removing Duplicates — duplicated(), drop_duplicates()
• Applying Functions — apply(), map(), applymap()
• String Operations on Columns — str.upper(), str.contains(), str.replace(), str.split()
• DateTime Handling — pd.to_datetime(), dt accessor
• Renaming Columns — rename()
• Changing Data Types — astype()
• Exporting Data — to_csv(), to_excel()

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📗 Chapter 19 — Matplotlib & Seaborn

Matplotlib

• Figure and Axes — plt.figure(), plt.subplot()
• Line Plot — plt.plot()
• Bar Chart — plt.bar(), plt.barh()
• Scatter Plot — plt.scatter()
• Histogram — plt.hist()
• Pie Chart — plt.pie()
• Titles, Labels, Legends, Grid — plt.title(), plt.xlabel(), plt.legend()
• Saving Figures — plt.savefig()

Seaborn

• sns.heatmap() — Correlation Matrix Visualization
• sns.pairplot() — Pairwise Relationships
• sns.boxplot() and sns.violinplot() — Distribution
• sns.barplot() and sns.countplot()
• sns.histplot() and sns.kdeplot()
• Seaborn Themes & Color Palettes

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🟠 Advanced Python — Chapters 20 to 21

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📙 Chapter 20 — OOPs (Object-Oriented Programming)

• Introduction to OOP — Why OOP?
• Classes & Objects
• __init__() Constructor
• Instance Variables vs Class Variables
• Instance Methods, Class Methods (@classmethod), Static Methods (@staticmethod)
• Inheritance — Single, Multi-level, Multiple
• super() Function
• Polymorphism — Method Overriding, Duck Typing
• Encapsulation — Private (__) and Protected (_) Members
• Abstraction — ABC module
• Dunder / Magic Methods — __str__(), __repr__(), __len__(), __add__()

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📙 Chapter 21 — Database Connectivity

• Introduction to Databases — Relational DB Concepts
• Connecting Python to SQLite — sqlite3 module
• Connecting Python to MySQL — mysql-connector-python
• Creating a Database & Tables
• CRUD Operations:
  • INSERT — Adding records
  • SELECT — Fetching records with conditions
  • UPDATE — Modifying records
  • DELETE — Removing records
• Using Cursors — cursor.execute(), fetchall(), fetchone()
• Commit & Rollback — Transaction Management
• Closing Connections Safely
• Parameterized Queries — Preventing SQL Injection

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🔴 Applied Projects

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🦠 Case Study — COVID Data Analysis

• Loading the COVID-19 Dataset
• Data Exploration & Understanding
• Cleaning & Preprocessing with Pandas
• Country-wise / Date-wise Analysis
• Trend Visualization using Matplotlib & Seaborn
• Key Insights & Conclusions

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📝 Python Test

• Chapter-wise Assessments
• Coding Challenges
• MCQ-style Revision Questions

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📅 Weekly

• Weekly Practice Problems
• Revision Notebooks
• Progressive Difficulty Exercises

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🚀 How to Use This Repo

1. Clone the repository

   git clone https://github.com/sachinkhote/Python-Learnings.git
   cd Python-Learnings

2. Open any chapter in Jupyter Notebook

   jupyter notebook

3. Follow chapters in order — especially for the first 13 chapters (core Python)

4. Libraries chapters (14–19) can be explored independently once core Python is done

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📌 Learning Path Recommendation

Basics          → Chapters 01–04
Data Structures → Chapters 05–06
Control Flow    → Chapters 07–09
Functions       → Chapter 10
Error Handling  → Chapter 11
Utilities       → Chapters 12–13
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Data Science    → Chapters 14–19
OOP             → Chapter 20
Databases       → Chapter 21
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Apply It All    → COVID Case Study

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This repository is dedicated to personal learning. Happy coding! 🎯