This repository is a structured guide to mastering NumPy Array Attributes, an essential foundation for understanding how arrays work in Python.
It focuses on exploring the properties and metadata of NumPy arrays, which are critical for data analysis, memory optimization, and efficient computation.
It features practical tasks that cover array shape, dimensions, size, datatype, memory consumption, and more. Perfect for beginners and learners who want hands-on practice with NumPy fundamentals.
| 🧪 Task File | 📝 Statement | 📄 Source Code | 🖥️ Output |
|---|---|---|---|
| Attributes of numpy.py |  |
 |
 |
- 📌 Understanding array shape, rows & columns
- 🔄 Checking the number of dimensions (
ndim) - ✖️ Counting total elements (
size) - 📊 Exploring data types (
dtype) of arrays - 💾 Measuring memory usage per element (
itemsize) - 🗂️ Calculating total memory consumption (
nbytes)
📊 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.
If you’re a recruiter, mentor, or fellow learner — let’s connect and grow together!
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A curated NumPy Array Attributes task covering shape, dimensions, size, data type, memory usage, and efficiency.
Each task includes source code and output screenshots to help learners build confidence in NumPy fundamentals for data analysis.