NumPy and Pandas for AI/ML

A hands-on learning repository covering fundamental and practical concepts of NumPy and Pandas for Artificial Intelligence, Machine Learning, Data Analysis, and Data Science.

This repository contains 15 structured Jupyter Notebooks covering NumPy operations, Pandas data analysis, data manipulation, missing value handling, performance optimization, and a final mini assessment.

---

📚 Topics Covered

NumPy

1. NumPy Basics
2. NumPy Mathematical Operations
3. NumPy Random Operations

Pandas

4. Pandas Basics
5. File Operations
6. Data Inspection
7. Data Selection
8. Data Manipulation
9. GroupBy and Aggregation
10. Merging and Combining Datasets
11. Date and Time Operations
12. String Operations
13. Missing Values
14. Performance Optimization
15. Mini Assessment

---

📂 Repository Structure

NumPy-Pandas-for-AI-ML/
│
├── 01_NumPy_Basics.ipynb
├── 02_NumPy_Math.ipynb
├── 03_NumPy_Random.ipynb
├── 04_Pandas_Basics.ipynb
├── 05_File_Operations.ipynb
├── 06_Data_Inspection.ipynb
├── 07_Data_Selection.ipynb
├── 08_Data_Manipulation.ipynb
├── 09_GroupBy.ipynb
├── 10_Merging_Data.ipynb
├── 11_Date_Time.ipynb
├── 12_String_Operations.ipynb
├── 13_Missing_Values.ipynb
├── 14_Performance_Optimization.ipynb
└── 15_Mini_Assessment01.ipynb

🎯 Learning Objectives

The objectives of this repository are to:

Build a strong foundation in NumPy and Pandas.
Understand NumPy arrays and efficient numerical operations.
Learn how to work with structured data using Pandas.
Practice data inspection, selection, filtering, and manipulation.
Understand grouping, aggregation, merging, and combining datasets.
Learn how to work with date, time, and string data.
Understand different strategies for handling missing values.
Learn performance optimization techniques in Pandas.
Apply concepts through practical coding exercises.
Assess overall understanding through a final mini assessment.
Build foundational skills for Data Analysis, Data Science, Artificial Intelligence, and Machine Learning.

📋 Assessment

The final notebook, Mini Assessment 01, contains practical and theoretical exercises covering the concepts learned throughout this repository.

The assessment includes topics such as:

Creating NumPy Arrays
NumPy Array vs Python List
Broadcasting
Pandas Series vs DataFrame
Reading CSV Files
Merging DataFrames
GroupBy and Aggregation
Filtering Rows Based on Conditions
Missing Value Handling
Statistical Measures Using NumPy
DataFrame Memory Optimization

👤 Author

Akash R

Aspiring Data Analyst and AI/ML Learner