Python for Data Science

Welcome to my Python for Data Science learning repository.

This repository documents my hands-on journey from fundamental Python programming to data science-ready coding, utilizing Jupyter Notebooks, practice sets, and modular mini-projects.

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Table of Contents

• Repository Structure
  • Fundamentals of Python
  • Advance Python
  • Exception Handling in Python
  • File Handling in Python
  • OOPS in Python
  • Python Fundamental Questions
  • My Projects
  • NumPy For Python
  • NumPy For Practice
  • NumPy Advance
  • Pandas in Python
  • Database from Kaggle
• Tech Stack
• Goal

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Repository Structure

Fundamentals of Python/

Core Python programming concepts:

• Control flow (if-else statements, for loops, while loops, nested loops, loop control mechanisms)
• Data structures (lists, tuples, sets, frozensets, dictionaries, strings)
• String operations and practice exercises
• Functions and arguments
• Lambda functions, list comprehensions, and dictionary comprehensions
• Sequence sum patterns
• Modules and operators

Advance Python/

• Decorators
• Namespace and scope management

Exception Handling in Python/

• Classification of Python errors
• Implementation of try, except, else, and finally blocks
• Creating and handling custom exceptions

File Handling in Python/

• Text file operations (read, write, append) and context management using the with statement
• Binary file operations
• Serialization and deserialization
• Pickling and unpickling Python objects

OOPS in Python/

Object-Oriented Programming principles:

• Classes and objects (Part 1 & 2)
• Reference variables and user-defined data types
• Inheritance hierarchies
• Encapsulation and data hiding
• Abstraction
• Polymorphism
• Object aggregation and the super() function

Python Fundamental Questions/

Practice notebooks dedicated to:

• Fundamental logic and level-1 problem solving
• List and dictionary manipulation exercises
• List comprehension practice
• Decorator practicals
• OOP practice
• Exception handling practicals

My Projects/

Functional mini-projects developed during the learning phase:

• Standard Calculator and Calculator V2
• ATM System simulation
• Library Management project
• DinosaursPedia
• Google Account Creation & Login simulation

NumPy For Python/

Fundamental array computing:

• Array creation and attributes
• Basic indexing and slicing
• Iteration and array reshaping
• Array stacking and splitting

NumPy For Practice/

Dedicated notebooks for reinforcing core NumPy concepts.

NumPy Advance/

In-depth exploration of advanced array operations and mathematical computing:

• Advanced Indexing: Techniques for complex array selection and multi-dimensional slicing.
• Array Broadcasting: Operational rules, implementation examples, and computational error resolution.
• Handling Missing Values: Identification, filtering, and management of NaN/null data points within numerical arrays.
• Plotting Graphs: Integrating array data with visualization operations.
• Set Functions: Advanced operations including union, intersection, and unique value extraction on arrays.
• Extra Methods (Part 1 & 2): Comprehensive coverage of specialized NumPy utility functions for extended statistical and mathematical operations.

Pandas in Python/

Comprehensive coverage of the Pandas library for data manipulation and analysis, organized into sub-folders:

Series in Pandas/

• Creation and structural understanding (Part 1 & 2)
• Indexing and slicing
• Math methods
• Plotting with built-in plot methods

DataFrames in Pandas/

• Introduction, creation, and structural understanding
• Filtering data and adding columns
• Editing the index and using Python functionality within Pandas
• Selecting columns, rows, and combined selections
• Math methods and statistical operations
• GroupBy: Aggregation, transformation, and group-level operations (Part 1 & 2), including hands-on exercises
• Merging, joining, and concatenation of DataFrames
• Important DataFrame methods (reference notebook)
• DataFrames deep-dive (CampusX series)

Practice in Pandas/

• Practice 1: YouTube channel analytics
• Practice 2: Cities by GDP analysis
• Practice 3: Student academic performance

Database from Kaggle/

A curated collection of real-world CSV datasets used across Pandas and NumPy notebooks:

Dataset	Description
batsman_runs_ipl.csv	IPL batsman run statistics
bollywood.csv	Bollywood movie data
cities_by_gdp.csv	Cities ranked by GDP
deliveries.csv	IPL ball-by-ball delivery data
diabetes.csv	Diabetes patient health metrics
global_top2000.csv	Global top 2000 companies
imdb-top-1000.csv	IMDB top 1000 movies
ipl-matches.csv	IPL match results
kohli_ipl.csv	Virat Kohli IPL performance stats
movies.csv	General movies dataset
student_performance_finalscore.csv	Student academic performance and final scores
subs.csv	Subscriber data

datasets / subfolder contains additional supporting files:

File	Description
courses.csv / courses.xlsx	Course catalogue data
deliveries.csv	Supplementary delivery data
matches.csv	Supplementary match data
reg-month1.csv	Registration data for month 1
reg-month2.csv / reg-month2.xlsx	Registration data for month 2
students.csv / students.xlsx	Student registration data

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Tech Stack

Tool	Purpose
Python 3	Core programming language
Jupyter Notebook	Interactive development environment
NumPy	Numerical computing
Pandas	Data manipulation and analysis
Matplotlib	Data visualization
Git & GitHub	Version control and hosting

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Goal

To establish a robust foundation in Python programming tailored for Data Science, bridging the gap between theoretical syntax and real-world data analysis and analytical projects.

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Maintained by Ayush Suthar