This repository is a structured guide to mastering NumPy basics and array creation, one of the most essential skills for data analysis and scientific computing in Python.
It features practical tasks covering 1D & 2D array creation, indexing, slicing, reshaping, and basic operations. Perfect for beginners and learners who want hands-on experience with NumPy.
| 🧪 Task File | 📄 Description | 📷 Output Screenshot |
|---|---|---|
| problem 1.py | ➕ Create NumPy arrays from Python lists |  |
| problem 2.py | 🔢 Create arrays using arange() |
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| problem 3.py | 🧮 Initialize arrays with zeros() & ones() |
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| problem 4.py | 🎛️ Reshape arrays using reshape() |
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| problem 5.py | 🔄 Indexing & slicing arrays |  |
| problem 6.py | ✖️ Element-wise arithmetic operations |  |
| problem 7.py | 🧊 Multi-dimensional arrays |  |
| problem 8.py | 🔍 Access rows, columns & elements |  |
| problem 9.py | 🔗 Iterating through arrays |  |
| problem 10.py | 📊 Combine arrays & flatten |  |
- 📌 Creating 1D and 2D arrays from lists and ranges
- 🔄 Indexing, slicing, and accessing array elements
- ✖️ Performing element-wise operations and arithmetic
- 🧊 Handling multi-dimensional arrays
- 🔗 Iterating, reshaping, and flattening arrays
- 📊 Combining arrays using NumPy utility functions
📊 Sunil Kumar Reddy Punnati
🎓 MCA Graduate | 💼 Data Analyst Intern
📍 Tirupati, India
💡 Passionate about Python, NumPy, and data analysis
🚀 Actively preparing for full-time roles in data analytics and software development
I believe in learning by doing, and this project reflects my commitment to mastering NumPy fundamentals with clean, structured coding.
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A curated set of NumPy tasks covering array creation, indexing, slicing, reshaping, and basic operations.
Each task includes code and output screenshots to help learners build confidence in NumPy fundamentals for data analysis.